Systems and methods for determining blood vessel parameters

ABSTRACT

A method for determining blood vessel parameters is provided. The method may include obtaining a blood vessel image of a target blood vessel. The method may also include generating a blood vessel model of the target blood vessel based on the blood vessel image. The blood vessel model is a grid model. The method may further include determining at least one blood vessel parameter of the target blood vessel based at least on the blood vessel model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of International Application No.PCT/CN2020/138408, filed on Dec. 22, 2020, which claims priority toChinese Patent Application No. 202010624356.8, filed on Jun. 30, 2020,and Chinese Patent Application No. 202010761635.9, filed on Jul. 31,2020, the contents of each of which are hereby incorporated byreference.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods fordetermining blood vessel parameters, and more particularly, to systemsand methods for determining blood vessel parameters using a trainedmodel.

BACKGROUND

Blood vessels are one of the most important organs of a human body. Ablood vessel imaging technology including, e.g., magnetic resonanceimaging (MRI), computed tomography (CT), etc., are widely used in thediagnosis of various vascular diseases, such as calcification, stenosis,aneurysms, etc.

SUMMARY

According to an aspect of the present disclosure, a method fordetermining blood vessel parameters may be provided. The method mayinclude obtaining a blood vessel image of a target blood vessel. Themethod may also include generating a blood vessel model of the targetblood vessel based on the blood vessel image. The blood vessel model maybe a grid model. The method may further include determining at least oneblood vessel parameter of the target blood vessel based at least on theblood vessel model.

In some embodiments, the method may include determining at least onemodel parameter of the blood vessel model based on feature informationof the target blood vessel. The method may further include generatingthe blood vessel model by performing a grid division on the blood vesselimage based on the at least one model parameter.

In some embodiments, the feature information of the target blood vesselmay include at least one of a type of the target blood vessel, adiameter of the target blood vessel, or a curvature of the target bloodvessel.

In some embodiments, the method may include obtaining at least one bloodflow parameter of the target blood vessel. The method may furtherinclude determining the at least one blood vessel parameter of thetarget blood vessel using a first trained model based on the bloodvessel model and the at least one blood flow parameter.

In some embodiments, the method may include obtaining at least one firstgrid node and at least one second grid node of the blood vessel model.The method may also include determining an initial value of the at leastone first grid node based on the at least one blood flow parameter. Themethod may also include determine an initial value of the at least onesecond grid node based on a preset value. The method may further includegenerating a preprocessed blood vessel model by preprocessing the bloodvessel model based on the initial value of the at least one first gridnode and the initial value of the at least one second grid node. Themethod may further include determining the at least one blood vesselparameter of the target blood vessel using the first trained model basedon the preprocessed blood vessel model.

In some embodiments, the first trained model may be a graph neuralnetwork model.

In some embodiments, the blood flow parameter may include at least oneof a blood density, a blood viscosity, an average blood flow velocity,an average blood flow volume, a blood pressure, or a cardiac output.

In some embodiments, the method may include obtaining a plurality ofgroups of first training samples. Each group of the plurality of groupsof first training samples may include a preprocessed sample blood vesselmodel of a sample blood vessel and at least one reference blood vesselparameter of the sample blood vessel. The method may further includegenerating the first trained model by training a first preliminary modelusing the plurality of groups of first training samples.

In some embodiments, for each group of the plurality of groups of firsttraining samples, the method may include obtaining a sample blood vesselimage of the sample blood vessel. The method may also includedetermining a sample blood vessel model of the sample blood vessel basedon the sample blood vessel image. The method may further includeobtaining at least one sample blood flow parameter of the sample bloodvessel. The method may further include generating the preprocessedsample blood vessel model by preprocessing the sample blood vessel modelbased on the at least one sample blood flow parameter.

In some embodiments, the method may further include determining the atleast one reference blood vessel parameter of the sample blood vesselbased on the sample blood vessel model and the at least one sample bloodflow parameter according to a first predetermined algorithm.

In some embodiments, the first predetermined algorithm may include acomputational fluid dynamics algorithm.

In some embodiments, the blood vessel parameter may include at least oneof a blood pressure, a blood flow volume, a blood vessel wall shearstress, a blood flow velocity, a blood flow direction, a blood flowresistance, or a center line of the target blood vessel.

In some embodiments, the method may include determining a mathematicalexpression corresponding to the blood vessel model. The method mayfurther include determining the at least one blood vessel parameter ofthe target blood vessel using a second trained model based on themathematical expression.

In some embodiments, the method may include determining the mathematicalexpression corresponding to the blood vessel model by performing anumerical processing operation on the blood vessel model.

In some embodiments, the blood vessel model may be a structured gridmodel.

In some embodiments, the method may include dividing the structured gridmodel into at least one first layer along an axial direction of theblood vessel model. The at least one first layer may include a pluralityof first grids. The method may further include determining themathematical expression based on first coordinate information of theplurality of first grids of the at least one first layer.

In some embodiments, the blood vessel model may be a structured surfacegrid model. The method may include, for each of the at least one firstlayer, determining a layer vector corresponding to the first layer basedon the first coordinate information of the plurality of first grids ofthe first layer. The method may further include determining a vectormatrix corresponding to the blood vessel model based on at least onelayer vector corresponding to the at least one first layer.

In some embodiments, the blood vessel model may be a structured volumegrid model. Each first layer may include a plurality of rows of grids.The method may include, for each row of the plurality of rows in eachfirst layer, determining a row vector corresponding to the row based onthe first coordinate information of the plurality of first grids of therow. The method may include, for each first layer of the at least onefirst layer, determining a layer vector matrix corresponding to thefirst layer based on a plurality of row vectors corresponding to theplurality of rows in the first layer. The method may further includedetermining a vector matrix corresponding to the blood vessel modelbased on at least one layer vector matrix corresponding to the at leastone first layer.

In some embodiments, the method may further include determining whethera count of elements in each row vector in each first layer is the same.In response to determining that a count of elements in a row vector in afirst layer is different from other row vectors in the first layer, themethod may further include complementing the elements in the row vectorsuch that the count of elements in each row vector in the each firstlayer is the same.

In some embodiments, the blood vessel model may be an unstructured gridmodel. The method may include mapping the unstructured grid model to astructured grid model. The method may also include dividing thestructured grid model into at least one second layer along an axialdirection of the structured grid model. The at least one second layermay include a plurality of second grids. The method may further includedetermining the mathematical expression based on second coordinateinformation of the plurality of second grids of the at least one secondlayer.

In some embodiments, the blood vessel model may be an unstructured gridmodel. The unstructured grid model may include a plurality of thirdgirds. The method may include determining the mathematical expressionbased on third coordinate information of the plurality of third girds.

In some embodiments, the method may include determining the at least oneblood vessel parameter of the target blood vessel by inputting themathematical expression into the second trained model.

In some embodiments, the method may include obtaining at least one bloodflow parameter of the target blood vessel. The method may furtherinclude determining the at least one blood vessel parameter of thetarget blood vessel by inputting the mathematical expression and the atleast one blood flow parameter into the second trained model.

In some embodiments, the method may include obtaining a plurality ofgroups of second training samples. Each group of the plurality of groupsof second training samples may include a sample mathematical expressioncorresponding to a sample blood vessel model of a sample blood vesseland at least one reference blood vessel parameter of the sample bloodvessel. The method may further include generating the second trainedmodel by training a second preliminary model using the plurality ofgroups of second training samples.

In some embodiments, the method may include, for each group of theplurality of groups of second training samples, obtaining a sample bloodvessel image of the sample blood vessel. The method may also includedetermining a sample blood vessel model of the sample blood vessel basedon the sample blood vessel image. The method may further includedetermining a sample mathematical expression corresponding to the sampleblood vessel model.

In some embodiments, the method may further include determining the atleast one reference blood vessel parameter of the sample blood vesselbased on the sample blood vessel model and at least one sample bloodflow parameter of the sample blood vessel according to a secondpredetermined algorithm.

In some embodiments, the second predetermined algorithm may include acomputational fluid dynamics algorithm.

According to another aspect of the present disclosure, a system fordetermining blood vessel parameters may be provided. The system mayinclude one or more storage devices and one or more processorsconfigured to communicate with the one or more storage devices. The oneor more storage devices may store executable instructions. When the oneor more processors execute the executable instructions, the one or moreprocessors may be directed to cause the system to perform a method. Themethod may include obtaining a blood vessel image of a target bloodvessel. The method may also include generating a blood vessel model ofthe target blood vessel based on the blood vessel image. The bloodvessel model may be a grid model. The method may further includedetermining at least one blood vessel parameter of the target bloodvessel based at least on the blood vessel model.

According to yet another aspect of the present disclosure, anon-transitory computer readable medium may be provided. Thenon-transitory computer readable may include at least one set ofinstructions for determining blood vessel parameters. When executed byone or more processors of a computing device, the at least one set ofinstructions may cause the computing device to perform a method. Themethod may include obtaining a blood vessel image of a target bloodvessel. The method may also include generating a blood vessel model ofthe target blood vessel based on the blood vessel image. The bloodvessel model may be a grid model. The method may further includedetermining at least one blood vessel parameter of the target bloodvessel based at least on the blood vessel model.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary medical systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device on which aprocessing device may be implemented according to some embodiments ofthe present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure;

FIG. 4A is a block diagram illustrating an exemplary processing device120A according to some embodiments of the present disclosure;

FIG. 4B is a block diagram illustrating an exemplary processing device120B according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for determininga blood vessel parameter of a target blood vessel according to someembodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determininga blood vessel parameter of a target blood vessel according to someembodiments of the present disclosure;

FIG. 7 is a schematic diagram illustrating an exemplary blood vesselmodel of a coronary artery according to some embodiments of the presentdisclosure;

FIG. 8 is a schematic diagram illustrating an exemplary process fordetermining at least one blood vessel parameter of a target blood vesselaccording to some embodiments of the present disclosure;

FIG. 9 is a flowchart illustrating an exemplary process for generating afirst trained model according to some embodiments of the presentdisclosure;

FIG. 10 is a schematic diagram illustrating an exemplary first trainedmodel according to some embodiments of the present disclosure;

FIG. 11 is a schematic diagram illustrating an exemplary process forgenerating a first trained model according to some embodiments of thepresent disclosure;

FIG. 12 is a flowchart illustrating an exemplary process for determininga blood vessel parameter of a target blood vessel according to someembodiments of the present disclosure;

FIG. 13 is a flowchart illustrating an exemplary process for determininga mathematical expression corresponding to a structured grid modelaccording to some embodiments of the present disclosure;

FIG. 14 is a schematic diagram illustrating different representations ofa subject according to some embodiments of the present disclosure;

FIG. 15 is a schematic diagram illustrating an exemplary structuredsurface grid model and an exemplary structured volume grid modelaccording to some embodiments of the present disclosure;

FIG. 16 is a schematic diagram illustrating an exemplary unstructuredsurface grid model and an exemplary unstructured volume grid modelaccording to some embodiments of the present disclosure;

FIG. 17A is a schematic diagram illustrating an exemplary process fordetermining a mathematical expression corresponding to a structuredsurface grid model according to some embodiments of the presentdisclosure;

FIG. 17B is a schematic diagram illustrating an exemplary process fordetermining a mathematical expression corresponding to a structuredvolume grid model according to some embodiments of the presentdisclosure;

FIG. 18 is a flowchart illustrating an exemplary process for generatinga second trained model according to some embodiments of the presentdisclosure; and

FIG. 19 is a schematic diagram illustrating an exemplary process forgenerating a second trained model according to some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments of the invention. As used herein, the singular forms “a,”“an,” and “the,” are intended to include the plural forms as well,unless the context clearly indicates otherwise. As used herein, theterms “and/or” and “at least one of” include any and all combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “comprises,” “comprising,” “includes,” and/or“including,” when used herein, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. Also, the term “exemplary” is intended to refer to an exampleor illustration.

It will be understood that the terms “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections or assembly of differentlevels in ascending order. However, the terms may be displaced byanother expression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices may be provided on a computer-readable medium, such asa compact disc, a digital video disc, a flash drive, a magnetic disc, orany other tangible medium, or as a digital download (and can beoriginally stored in a compressed or installable format that needsinstallation, decompression, or decryption prior to execution). Suchsoftware code may be stored, partially or fully, on a storage device ofthe executing computing device, for execution by the computing device.Software instructions may be embedded in firmware, such as an EPROM. Itwill be further appreciated that hardware modules/units/blocks may beincluded in connected logic components, such as gates and flip-flops,and/or can be included of programmable units, such as programmable gatearrays or processors. The modules/units/blocks or computing devicefunctionality described herein may be implemented as softwaremodules/units/blocks, but may be represented in hardware or firmware. Ingeneral, the modules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that, although the terms “first,” “second,”“third,” etc., may be used herein to describe various elements, theseelements should not be limited by these terms. These terms are only usedto distinguish one element from another. For example, a first elementcould be termed a second element, and, similarly, a second element couldbe termed a first element, without departing from the scope of exemplaryembodiments of the present disclosure.

Spatial and functional relationships between elements are describedusing various terms, including “connected,” “attached,” and “mounted.”Unless explicitly described as being “direct,” when a relationshipbetween first and second elements is described in the presentdisclosure, that relationship includes a direct relationship where noother intervening elements are present between the first and secondelements, and also an indirect relationship where one or moreintervening elements are present (either spatially or functionally)between the first and second elements. In contrast, when an element isreferred to as being “directly” connected, attached, or positioned toanother element, there are no intervening elements present. Other wordsused to describe the relationship between elements should be interpretedin a like fashion (e.g., “between,” versus “directly between,”“adjacent,” versus “directly adjacent,” etc.).

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The term “image” in the present disclosure is used to collectively referto image data (e.g., scan data, projection data) and/or images ofvarious forms, including a two-dimensional (2D) image, athree-dimensional (3D) image, a four-dimensional (4D), etc. The term“pixel” and “voxel” in the present disclosure are used interchangeablyto refer to an element of an image. The term “anatomical structure” inthe present disclosure may refer to gas (e.g., air), liquid (e.g.,water), solid (e.g., stone), cell, tissue, organ of a subject, or anycombination thereof, which may be displayed in an image and really existin or on the subject's body. The term “region,” “location,” and “area”in the present disclosure may refer to a location of an anatomicalstructure shown in the image or an actual location of the anatomicalstructure existing in or on the subject's body, since the image mayindicate the actual location of a certain anatomical structure existingin or on the subject's body. The term “an image of a subject” may bereferred to as the subject for brevity.

An aspect of the present disclosure relates to systems and methods fordetermining blood vessel parameters. A processing device may obtain ablood vessel image of a target blood vessel. The processing device maygenerate a blood vessel model of the target blood vessel based on theblood vessel image. For example, the processing device may perform agrid division on the blood vessel image to generate the blood vesselmodel. The processing device may determine at least one blood vesselparameter of the target blood vessel based on the blood vessel model.For example, the processing device may determine the at least one bloodvessel parameter of the target blood vessel based on the blood vesselmodel and at least one blood flow parameter using a first trained model(e.g., a graph neural network model). As another example, the processingdevice may determine a mathematical expression corresponding to theblood vessel model, and determine the at least one blood vesselparameter of the target blood vessel based on the mathematicalexpression using a second trained model.

Compared with a blood vessel parameter determination approach thatdirectly uses a computational fluid dynamics (CFD) algorithm todetermine blood vessel parameters based on a blood vessel image, thesystems and methods as disclosed herein may simplify the blood vesselparameter determination process by inputting the blood vessel model intothe first trained model or inputting the mathematical expressioncorresponding to the blood vessel model into the second trained model,and reduce the requirement for the grid quality and boundary conditionsof the blood vessel model. Thus, the efficiency and/or accuracy of theblood vessel parameter determination may further be improved. Inaddition, in a training process of the first trained model or the secondtrained model, one or more reference blood vessel parameters of a sampleblood vessel determined based on a sample blood vessel model and atleast one sample blood flow parameter of the sample blood vesselaccording to the CFD algorithm may be used as a ground truth, therebyincorporating the benefits of the CFD algorithm in and improving theaccuracy of the first trained model or the second trained model.

FIG. 1 is a schematic diagram illustrating an exemplary medical systemaccording to some embodiments of the present disclosure. As illustratedin FIG. 1 , the medical system 100 may include a medical device 110, aprocessing device 120, a storage device 130, a terminal device 140, anda network 150. In some embodiments, two or more components of themedical system 100 may be connected to and/or communicate with eachother via a wireless connection, a wired connection, or a combinationthereof. The medical system 100 may include various types of connectionbetween its components. For example, the medical device 110 may beconnected to the processing device 120 through the network 150, orconnected to the processing device 120 directly as illustrated by thebidirectional dotted arrow connecting the medical device 110 and theprocessing device 120 in FIG. 1 . As another example, the terminaldevice 140 may be connected to the processing device 120 through thenetwork 150, or connected to the processing device 120 directly asillustrated by the bidirectional dotted arrow connecting the terminaldevice 140 and the processing device 120 in FIG. 1 . As still anotherexample, the storage device 130 may be connected to the medical device110 through the network 150, or connected to the medical device 110directly as illustrated by the bidirectional dotted arrow connecting themedical device 110 and the storage device 130 in FIG. 1 . As stillanother example, the storage device 130 may be connected to the terminaldevice 140 through the network 150, or connected to the terminal device140 directly as illustrated by the bidirectional dotted arrow connectingthe terminal device 140 and the storage device 130 in FIG. 1 .

The medical device 110 may be configured to acquire imaging datarelating to a subject. The imaging data relating to a subject mayinclude an image (e.g., an image slice), projection data, or acombination thereof. In some embodiments, the imaging data may be atwo-dimensional (2D) imaging data, a three-dimensional (3D) imagingdata, a four-dimensional (4D) imaging data, or the like, or anycombination thereof. The subject may be biological or non-biological.For example, the subject may include a patient, a man-made object, etc.As another example, the subject may include a specific portion, anorgan, and/or tissue of the patient. Specifically, the subject mayinclude the head, the neck, the thorax, the heart, the stomach, a bloodvessel, soft tissue, a tumor, or the like, or any combination thereof.In the present disclosure, “object” and “subject” are usedinterchangeably.

In some embodiments, the medical device 110 may include a singlemodality imaging device. For example, the medical device 110 may includea positron emission tomography (PET) device, a single-photon emissioncomputed tomography (SPECT) device, a magnetic resonance imaging (MRI)device (also referred to as an MR device, an MR scanner), a computedtomography (CT) device, an ultrasound (US) device, an X-ray imagingdevice, a digital subtraction angiography (DSA) device, a magneticresonance angiography (MRA) device, a computed tomography angiography(CTA) device, or the like, or any combination thereof. In someembodiments, the medical device 110 may include a multi-modality imagingdevice. Exemplary multi-modality imaging devices may include a PET-CTdevice, a PET-MRI device, a SPET-CT device, or the like, or anycombination thereof. The multi-modality imaging device may performmulti-modality imaging simultaneously. For example, the PET-CT devicemay generate structural X-ray CT data and functional PET datasimultaneously in a single scan. The PET-MRI device may generate MRIdata and PET data simultaneously in a single scan.

In some embodiments, the medical device 110 may transmit the image(s)via the network 150 to the processing device 120, the storage device130, and/or the terminal device 140. For example, the image(s) may besent to the processing device 120 for further processing or may bestored in the storage device 130.

The processing device 120 may process data and/or information. The dataand/or information may be obtained from the medical device 110 orretrieved from the storage device 130, the terminal device 140, and/oran external device (external to the medical system 100) via the network150. For example, the processing device 120 may obtain an original imageof a target blood vessel. As another example, the processing device 120may determine a portion of an original image including a target bloodvessel as a blood vessel image. As still another example, the processingdevice 120 may generate a blood vessel model of a target blood vesselbased on a blood vessel image. As still another example, the processingdevice 120 may determine at least one blood vessel parameter of a targetblood vessel based at least on a blood vessel model. In someembodiments, the processing device 120 may be a single server or aserver group. The server group may be centralized or distributed. Insome embodiments, the processing device 120 may be local or remote. Forexample, the processing device 120 may access information and/or datafrom the medical device 110, the storage device 130, and/or the terminaldevice 140 via the network 150. As another example, the processingdevice 120 may be directly connected to the medical device 110, theterminal device 140, and/or the storage device 130 to access informationand/or data. In some embodiments, the processing device 120 may beimplemented on a cloud platform. For example, the cloud platform mayinclude a private cloud, a public cloud, a hybrid cloud, a communitycloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like,or any combination thereof. In some embodiments, the processing device120 may be part of the terminal device 140. In some embodiments, theprocessing device 120 may be part of the medical device 110. In someembodiments, other hardware and/or software modules may be used inconjunction with the processing device 120, including but not limited toa microcode, a device driver, a redundant processing unit, an externaldisk drive array, a RAID system, a tape drive, and a data backup storagesystem.

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the medical device 110, the processing device 120, and/orthe terminal device 140. The data may include image data acquired by theprocessing device 120, algorithms and/or models for processing the imagedata, etc. For example, the storage device 130 may store an originalimage of a target blood vessel obtained from a medical device (e.g., themedical device 110). As another example, the storage device 130 maystore a blood vessel image of a target blood vessel determined by theprocessing device 120. As still another example, the storage device 130may store a blood vessel model of a target blood vessel determined bythe processing device 120. As still another example, the storage device130 may store at least one blood vessel parameter of a target bloodvessel determined by the processing device 120. In some embodiments, thestorage device 130 may store data and/or instructions that theprocessing device 120, and/or the terminal device 140 may execute or useto perform exemplary methods described in the present disclosure. Insome embodiments, the storage device 130 may include a mass storage, aremovable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagesmay include a magnetic disk, an optical disk, a solid-state drive, etc.Exemplary removable storages may include a flash drive, a floppy disk,an optical disk, a memory card, a zip disk, a magnetic tape, etc.Exemplary volatile read-and-write memories may include a random-accessmemory (RAM). Exemplary RAM may include a dynamic RAM (DRAM), a doubledate rate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), athyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. ExemplaryROM may include a mask ROM (MROM), a programmable ROM (PROM), anerasable programmable ROM (EPROM), an electrically erasable programmableROM (EEPROM), a compact disk ROM (CD-ROM), and a digital versatile diskROM, etc. In some embodiments, the storage device 130 may be implementedon a cloud platform. Merely by way of example, the cloud platform mayinclude a private cloud, a public cloud, a hybrid cloud, a communitycloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like,or any combination thereof.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in themedical system 100 (e.g., the processing device 120, the terminal device140). One or more components in the medical system 100 may access thedata or instructions stored in the storage device 130 via the network150. In some embodiments, the storage device 130 may be integrated intothe medical device 110 or the terminal device 140.

The terminal device 140 may be connected to and/or communicate with themedical device 110, the processing device 120, and/or the storage device130. In some embodiments, the terminal device 140 may include a mobiledevice 141, a tablet computer 142, a laptop computer 143, or the like,or any combination thereof. For example, the mobile device 141 mayinclude a mobile phone, a personal digital assistant (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, a laptop, atablet computer, a desktop, or the like, or any combination thereof. Insome embodiments, the terminal device 140 may include an input device,an output device, etc. The input device may include alphanumeric andother keys that may be input via a keyboard, a touchscreen (for example,with haptics or tactile feedback), a speech input, an eye trackinginput, a brain monitoring system, or any other comparable inputmechanism. Other types of the input device may include a cursor controldevice, such as a mouse, a trackball, or cursor direction keys, etc. Theoutput device may include a display, a printer, or the like, or anycombination thereof.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the medical system 100. In someembodiments, one or more components of the medical system 100 (e.g., themedical device 110, the processing device 120, the storage device 130,the terminal device 140, etc.) may communicate information and/or datawith one or more other components of the medical system 100 via thenetwork 150. For example, the processing device 120 and/or the terminaldevice 140 may obtain an original image of a target blood vessel fromthe medical device 110 via the network 150. As another example, theprocessing device 120 and/or the terminal device 140 may obtaininformation stored in the storage device 130 via the network 150. Thenetwork 150 may be and/or include a public network (e.g., the Internet),a private network (e.g., a local area network (LAN), a wide area network(WAN), etc.), a wired network (e.g., an Ethernet network), a wirelessnetwork (e.g., a Wi-Fi network), a cellular network (e.g., a Long TermEvolution (LTE) network), a frame relay network, a virtual privatenetwork (VPN), a satellite network, a telephone network, routers, hubs,witches, server computers, and/or any combination thereof. For example,the network 150 may include a cable network, a wireline network, afiber-optic network, a telecommunications network, an intranet, awireless local area network (WLAN), a metropolitan area network (MAN), apublic telephone switched network (PSTN), a Bluetooth™ network, aZigBee™ network, a near field communication (NFC) network, or the like,or any combination thereof. In some embodiments, the network 150 mayinclude one or more network access points. For example, the network 150may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the medical system 100 may be connected to the network 150to exchange data and/or information. In some embodiments, one or morecomponents (e.g., the processing device 120) of the medical system 100may communicate with the network 150 via a network adapter. The one ormore components (e.g., the processing device 120) of the medical system100 may communicate with the network adapter via a bus.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. However, thosevariations and modifications do not depart the scope of the presentdisclosure. In some embodiments, the medical system 100 may include oneor more additional components and/or one or more components of themedical system 100 described above may be omitted. For example, themedical system 100 may include a cache memory, a data backup storagesystem, or the like. Additionally or alternatively, two or morecomponents of the medical system 100 may be integrated into a singlecomponent. A component of the medical system 100 may be implemented ontwo or more sub-components.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device on which theprocessing device 120 may be implemented according to some embodimentsof the present disclosure. As illustrated in FIG. 2 , a computing device200 may include a processor 210, storage 220, an input/output (I/O) 230,and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataobtained from the medical device 110, the terminal device 140, thestorage device 130, and/or any other component of the medical system100. In some embodiments, the processor 210 may include one or morehardware processors, such as a microcontroller, a microprocessor, areduced instruction set computer (RISC), an application specificintegrated circuits (ASICs), an application-specific instruction-setprocessor (ASIP), a central processing unit (CPU), a graphics processingunit (GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combination thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both process A and process B, it should be understood thatprocess A and process B may also be performed by two or more differentprocessors jointly or separately in the computing device 200 (e.g., afirst processor executes process A and a second processor executesprocess B, or the first and second processors jointly execute processesA and B).

The storage 220 may store data/information obtained from the medicaldevice 110, the terminal device 140, the storage device 130, and/or anyother component of the medical system 100. The storage 220 may besimilar to the storage device 130 described in connection with FIG. 1 ,and the detailed descriptions are not repeated here.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Examples of the input device mayinclude a keyboard, a mouse, a touchscreen, a microphone, a soundrecording device, or the like, or a combination thereof. Examples of theoutput device may include a display device, a loudspeaker, a printer, aprojector, or the like, or a combination thereof. Examples of thedisplay device may include a liquid crystal display (LCD), alight-emitting diode (LED)-based display, a flat panel display, a curvedscreen, a television device, a cathode ray tube (CRT), a touchscreen, orthe like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and themedical device 110, the terminal device 140, and/or the storage device130. The connection may be a wired connection, a wireless connection,any other communication connection that can enable data transmissionand/or reception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee link, a mobile networklink (e.g., 3G, 4G, 5G), or the like, or any combination thereof. Insome embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure. In some embodiments, the terminaldevice 140 and/or the processing device 120 may be implemented on amobile device 300, respectively.

As illustrated in FIG. 3 , the mobile device 300 may include acommunication platform 310, a display 320, a graphics processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, and storage 390. In some embodiments, any other suitable component,including but not limited to a system bus or a controller (not shown),may also be included in the mobile device 300. The bus may include oneor more types of bus structures (e.g., a memory bus, or a local bus usedin a memory controller, a peripheral bus, a graphics acceleration port,or a processor). For example, the bus structures may include an industrystandard architecture (ISA) bus, a microchannel architecture (MAC) bus,an enhanced ISA bus, a video electronics standards association (VESA)local bus, a peripheral component interconnection (PCI) bus, or thelike.

In some embodiments, the communication platform 310 may be configured toestablish a connection between the mobile device 300 and othercomponents of the medical system 100, and enable data and/or signal tobe transmitted between the mobile device 300 and other components of themedical system 100. For example, the communication platform 310 mayestablish a wireless connection between the mobile device 300 and themedical device 110, and/or the processing device 120. The wirelessconnection may include, for example, a Bluetooth™ link, a Wi-Fi™ link, aWiMax™ link, a WLAN link, a ZigBee link, a mobile network link (e.g.,3G, 4G, 5G), or the like, or any combination thereof. The communicationplatform 310 may also enable the data and/or signal between the mobiledevice 300 and other components of the medical system 100. For example,the communication platform 310 may transmit data and/or signals inputtedby a user to other components of the medical system 100. The inputteddata and/or signals may include a user instruction. As another example,the communication platform 310 may receive data and/or signalstransmitted from the processing device 120. The received data and/orsignals may include imaging data acquired by the medical device 110.

In some embodiments, a mobile operating system (OS) 370 (e.g., iOS™Android™, Windows Phone™, etc.) and one or more applications (App(s))380 may be loaded into the memory 360 from the storage 390 in order tobe executed by the CPU 340. The applications 380 may include a browseror any other suitable mobile apps for receiving and renderinginformation from the processing device 120. User interactions with theinformation stream may be achieved via the I/O 350 and provided to theprocessing device 120 and/or other components of the medical system 100via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or another type of work station or terminaldevice, although a computer may also act as a server if appropriatelyprogrammed. It is believed that those skilled in the art are familiarwith the structure, programming and general operation of such computerequipment and as a result the drawings should be self-explanatory.

FIG. 4A is a block diagram illustrating an exemplary processing device120A according to some embodiments of the present disclosure. FIG. 4B isa block diagram illustrating an exemplary processing device 120Baccording to some embodiments of the present disclosure. The processingdevice 120A may be configured to determine at least one blood vesselparameter of a target blood vessel based on a blood vessel model using afirst trained model or a second trained model. In some embodiments, theprocessing device 120B may be configured to process information and/ordata to generate a plurality of training samples (e.g., a plurality ofgroups of first training samples, a plurality of groups of secondtraining samples). The processing device 120B may further be configuredto generate a first trained model and/or a second trained model usingthe plurality of training samples.

In some embodiments, the processing devices 120A and 120B may berespectively implemented on a processing unit (e.g., a processor 210illustrated in FIG. 2 or a CPU 340 as illustrated in FIG. 3 ). Merely byway of example, the processing devices 120A may be implemented on acomputing device 200, and the processing device 120B may be implementedon a CPU 340 of a terminal device. Alternatively, the processing devices120A and 120B may be implemented on a same computing device 200 or asame CPU 340. For example, the processing devices 120A and 120B may beimplemented on a same computing device 200.

As shown in FIG. 4A, the processing device 120A may include an obtainingmodule 410, a blood vessel model generation module 420, and a bloodvessel parameter determination module 430.

The obtaining module 410 may be configured to obtain data and/orinformation associated with the medical system 100. The data and/orinformation associated with the medical system 100 may include a bloodvessel image, a blood vessel model, at least one blood flow parameter,at least one blood vessel parameter, or the like, or any combinationthereof. For example, the obtaining module 410 may obtain a blood vesselimage of a target blood vessel. As another example, the obtaining module410 may obtain at least one blood flow parameter of a target bloodvessel. In some embodiments, the obtaining module 410 may obtain thedata and/or the information associated with the medical system 100 fromone or more components (e.g., the medical device 110, the storage device130, the terminal 140) of the medical system 100 via the network 150.

The blood vessel model generation module 420 may be configured togenerate a blood vessel model of a target blood vessel. In someembodiments, the blood vessel model generation module 420 may generate ablood vessel model of a target blood vessel based on a blood vesselimage. For example, the blood vessel model generation module 420 maydetermine at least one model parameter of a blood vessel model based onfeature information of a target blood vessel. The blood vessel modelgeneration module 420 may generate the blood vessel model by performing,based on the at least one model parameter, a grid division on a bloodvessel image. More descriptions for the generation of the blood vesselmodel may be found elsewhere in the present disclosure (e.g., FIG. 5 andthe descriptions thereof).

The blood vessel parameter determination module 430 may be configured todetermine at least one blood vessel parameter of a target blood vesselbased at least on a blood vessel model. For example, the blood vesselparameter determination module 430 may determine, based on a bloodvessel model and at least one blood flow parameter, at least one bloodvessel parameter of a target blood vessel using a first trained model.More descriptions for the determination of the at least one blood vesselparameter of the target blood vessel using a first trained model may befound elsewhere in the present disclosure (e.g., FIG. 6 and thedescriptions thereof). As another example, the blood vessel parameterdetermination module 430 may determine a mathematical expressioncorresponding to a blood vessel model. The blood vessel parameterdetermination module 430 may then determine at least one blood vesselparameter of a target blood vessel using a second trained model based onthe mathematical expression. More descriptions for the determination ofthe at least one blood vessel parameter of the target blood vessel usinga second trained model may be found elsewhere in the present disclosure(e.g., FIG. 12 and the descriptions thereof).

As shown in FIG. 4B, the processing device 120B may include an obtainingmodule 440, and a trained model generation module 450.

The obtaining module 440 may be configured to obtain data and/orinformation associated with a first trained model and/or a secondtrained model. For example, the obtaining module 440 may obtain aplurality of groups of first training samples for training a firsttrained model. Each group of the plurality of groups of first trainingsamples may include a preprocessed sample blood vessel model of a sampleblood vessel and at least one reference blood vessel parameter of thesample blood vessel. More descriptions for the plurality of groups offirst training samples may be found elsewhere in the present disclosure(e.g., FIG. 9 and the descriptions thereof). As another example, theobtaining module 440 may obtain a plurality of groups of second trainingsamples for training a second trained model. Each group of the pluralityof groups of second training samples may include a sample mathematicalexpression corresponding to a sample blood vessel model of a sampleblood vessel and at least one reference blood vessel parameter of thesample blood vessel. More descriptions for the plurality of groups ofsecond training samples may be found elsewhere in the present disclosure(e.g., FIG. 18 and the descriptions thereof).

The trained model generation module 450 may be configured to generate atrained model. For example, the trained model generation module 450 maygenerate a first trained model by training a first preliminary modelusing a plurality of groups of first training samples. More descriptionsfor the generation of the first trained model may be found elsewhere inthe present disclosure (e.g., FIG. 9 and the descriptions thereof). Asanother example, the trained model generation module 450 may generate asecond trained model by training a second preliminary model using aplurality of groups of second training samples. More descriptions forthe generation of the second trained model may be found elsewhere in thepresent disclosure (e.g., FIG. 18 and the descriptions thereof).

It should be noted that the above description of the processing device120A and the processing device 120B are merely provided for the purposesof illustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more modules may be added or omitted in the processing device120A and/or the processing device 120B. For example, the processingdevice 120A may further include a storage module (not shown in FIG. 4A)configured to store data and/or information (e.g., an original image, ablood vessel image, a blood vessel model, a blood vessel parameter)associated with the medical system 100. As another example, theprocessing device 120A may further include a training module 440 (notshown in FIG. 4A) configured to generate a first trained model and/or asecond trained model using a plurality of training samples. In someembodiments, two or more modules may be integrated into a single module.For example, the blood vessel model generation module 420 and the bloodvessel parameter determination module 430 may be integrated into asingle module.

FIG. 5 is a flowchart illustrating an exemplary process for determininga blood vessel parameter of a target blood vessel according to someembodiments of the present disclosure. In some embodiments, the process500 may be implemented in the medical system 100 illustrated in FIG. 1 .For example, the process 500 may be stored in the storage device 130and/or the storage (e.g., the storage 220, the storage 390) as a form ofinstructions, and invoked and/or executed by the processing device 120A(e.g., the processor 210 of the computing device 200 as illustrated inFIG. 2 , the CPU 340 of the mobile device 300 as illustrated in FIG. 3). The operations of the illustrated process presented below areintended to be illustrative. In some embodiments, the process 500 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 500 as illustrated inFIG. 5 and described below is not intended to be limiting.

In 510, the processing device 120A (e.g., the obtaining module 410) mayobtain a blood vessel image of a target blood vessel.

As used herein, a target blood vessel refers to a blood vessel ofinterest or a part thereof. For example, the blood vessel may include anentire coronary artery, a branch of a coronary artery, an entrancecross-section of a coronary artery, or the like. In some embodiments,the target blood vessel may include one or more blood vessels of a sametype or different types. For example, the target blood vessel mayinclude an aortic vessel, a coronary artery, an abdominal artery, abrain artery, a lower extremity artery, a neck vessel, or the like, orany combination thereof. In some embodiments, the target blood vesselmay be associated with a lesion that needs to be analyzed. For example,the target blood vessel may include a blood vessel associated with anaortic dissection, a hemangioma, or the like.

In some embodiments, the processing device 120A may obtain an originalimage of the target blood vessel. The original image of the target bloodvessel refers to image data corresponding to the entire target bloodvessel. In some embodiments, the original image of the target bloodvessel may include a two-dimensional (2D) image, a three-dimensional(3D) image, a four-dimensional (4D) image (e.g., a series of 3D imagesover time), and/or any related image data. In some embodiments, theoriginal image of the target blood vessel may include color image data,point-cloud data, depth image data, mesh data, scan data, projectiondata, or the like, or any combination thereof, of the target bloodvessel.

In some embodiments, the original image of the target blood vessel maybe captured by a medical device (e.g., the medical device 110). Theoriginal image of the target blood vessel may include a medical image.For example, the original image of the target blood vessel may include aCT image, an MRI image, a PET image, an ultrasound (US) image, an X-rayimage, a DSA image, an MRA image, a CTA image, or the like. In someembodiments, the processing device 120A may obtain the original image ofthe target blood vessel from the medical device. Additionally oralternatively, the original image of the target blood vessel may beacquired by the medical device, and stored in a storage device (e.g.,the storage device 130, the storage 220, the storage 390, or an externalsource). The processing device 120A may retrieve the original image ofthe target blood vessel from the storage device.

Further, the processing device 120A may determine a portion of theoriginal image including the target blood vessel as the blood vesselimage. For example, if the target blood vessel is a coronary artery, theprocessing device 120A may segment an image that includes the coronaryartery from the original image to generate a blood vessel image of thecoronary artery. In some embodiments, the blood vessel image may includea 2D image, a 3D image, or the like. For example, if the original imageis a 3D image of the target blood vessel, and the blood vessel image maybe a 3D image or a 2D image corresponding to the target blood vessel. Asanother example, if the original image is a 2D image, and the bloodvessel image may be a 2D image corresponding to the target blood vessel.

In some embodiments, the blood vessel image may be automaticallysegmented from the original image by the processing device 120Aaccording to an image analysis algorithm (e.g., an image segmentationalgorithm). Exemplary image segmentation algorithm may include asegmentation algorithm based on genetic algorithms, an edge detectionalgorithm, a segmentation algorithm based on regions (e.g., a regiongrowing segmentation algorithm, a thresholding segmentation algorithm, aclustering segmentation algorithm), a segmentation algorithm based onactive contour model, a segmentation algorithm based on mathematicalmorphology, a segmentation algorithm based on statistics, a segmentationalgorithm based on machine learning, or the like, or any combinationthereof. Additionally or alternatively, the blood vessel image may bemanually segmented from the original image by a user (e.g., a doctor, animaging specialist, a technician) of the medical system 100.Additionally or alternatively, the blood vessel image may be segmentedfrom the original image by the processing device 120A semi-automaticallybased on an image analysis algorithm in combination with informationprovided by a user. Exemplary information provided by the user mayinclude a parameter relating to the image analysis algorithm, a positionparameter relating to a region to be segmented, an adjustment to,rejection, or confirmation of a preliminary segmentation resultgenerated by the processing device 120A, or the like.

In 520, the processing device 120A (e.g., the blood vessel modelgeneration module 420) may generate, based on the blood vessel image, ablood vessel model of the target blood vessel.

As used herein, a blood vessel model of a target blood vessel refers toa model of the target blood vessel representing the morphology of thetarget blood vessel. In some embodiments, the blood vessel model mayinclude a 2D model (e.g., a 2D grid model), a 3D model (e.g., a 3D gridmodel), or the like. The 2D grid model or 3D grid model of the targetblood vessel may include a plurality of grids (e.g., a plurality of gridnodes, edges, and faces) that define a 2D shape or a 3D shape of thetarget blood vessel. Compared to using a plurality of pixels or voxelsto represent the morphology of the target blood vessel, the morphologyof the target blood vessel may be represented more accurately using theplurality of grids. For example, as illustrated in FIG. 14 , an ellipse1410, an ellipse 1420, and an ellipse 1430 represent a same subject(e.g., a target blood vessel). The representation 1410 includes aplurality of pixels 1440 of a relatively large size. The representation1420 includes a plurality of pixels 1450 of a relatively small size. Therepresentation 1430 includes a plurality of grids. Compared to using aplurality of pixels to represent the shape of the subject, the shape ofthe subject may be represented more accurately using the plurality ofgrids, as shown in FIG. 14 .

In some embodiments, the blood vessel model may include an unstructuredgrid model (e.g., an unstructured surface grid model, an unstructuredvolume grid model), a structured grid model (e.g., a structured surfacegrid model, a structured volume grid model), or the like, or anycombination thereof. As used herein, an unstructured (or irregular) gridrefers to a tessellation of a part of a Euclidean plane or a Euclideanspace by shapes, such as triangles or tetrahedra, in an irregularpattern. As used herein, a structured (or regular) grid refers to atessellation of a part of a Euclidean plane or a Euclidean space byshapes, such as quadrilaterals or cuboids, in a regular or periodicalpattern. That is, the structured grid refers to that elements (e.g.,grids) are well ordered, and a simple scheme repeats in a structuredgrid and can be used to label elements and identify neighbors. Merely byway of example, each grid in a 2D structured grid model may have fourneighbor grids. In the unstructured grid, elements (e.g., grids) can bejoined in any manner, and special lists may be determined to identifyneighboring elements.

As used herein, a surface grid model refers to a model that onlycontains grids on the surface of the model. As used herein, a volumegrid model refers to a model that contains grids on both the surface andthe interior of the model. For example, in FIG. 15, 1510 illustrates astructured surface grid model, and 1520 illustrates structured volumegrid model. As another example, in FIG. 16, 1610 illustrates anunstructured surface grid model, and 1620 illustrates an unstructuredvolume grid model.

In some embodiments, the processing device 120A may determine at leastone model parameter of the blood vessel model based on featureinformation of the target blood vessel. The feature information of thetarget blood vessel may include a type of the target blood vessel, adiameter of the target blood vessel, a curvature of the target bloodvessel, a direction of the target blood vessel, an exit area of thetarget blood vessel, an entrance area of the target blood vessel, or thelike, or any combination thereof. As used herein, a curvature of a bloodvessel refers to an angle between a line along a tangent direction of aspecific point on a surface of the blood vessel and an arc lengthcorresponding to the point, which can be used to describe a degree ofdeviation of a curve from a straight line. As used herein, a directionof a blood vessel refers to a direction of blood flow in the bloodvessel. In some embodiments, the processing device 120A may determinethe feature information (or a portion thereof) of the target bloodvessel based on the original image and/or the blood vessel image of thetarget blood vessel according to an image analysis algorithm (e.g., animage segmentation algorithm, a feature extraction algorithm).Additionally or alternatively, the feature information (or a portionthereof) of the target blood vessel may be previously generated andstored in a storage device (e.g., the storage device 130, the storage220, the storage 390, or an external source). The processing device 120Amay retrieve the feature information (or a portion thereof) of thetarget blood vessel from the storage device.

In some embodiments, the at least one model parameter of the bloodvessel model may include a grid density, a size of a grid, a shape of agrid, a grid count, or the like, or any combination thereof. As usedherein, a grid density refers to a number (or count) of grids per unitarea of a model. In some embodiments, the processing device 120A maydetermine at least one parameter value of the at least one modelparameter based on the feature information of the target blood vesseland a relationship between feature information and at least one modelparameter. For example, the relationship may be represented in the formof a table recording different feature information and theircorresponding value(s) of the model parameter(s). The relationshipbetween the feature information and the model parameter(s) may be storedin a storage device, and the processing device 120A may retrieve therelationship from the storage device. In some embodiments, therelationship between the feature information and the model parameter(s)may be determined by the processing device 120A based on experimentaldata or user experience.

In some embodiments, the processing device 120A may determine the griddensity of the blood vessel model based on the type of the target bloodvessel. The grid density of the blood vessel model corresponding to anaortic blood vessel may be smaller than the grid density of the bloodvessel model corresponding to a coronary blood vessel. For illustrationpurposes, the target blood vessel may include an aortic blood vessel anda coronary blood vessel. The processing device 120A may divide a part ofthe blood vessel model corresponding to the aortic blood vessel usinggrids of one or more relatively large grid sizes. The processing device120A may divide a part of the blood vessel model corresponding to thecoronary blood vessel (especially a branch of the coronary blood vessel)using grids of one or more relatively small grid sizes. Accordingly,compared to a method of dividing blood vessel models corresponding todifferent types of blood vessels of a same grid density, the systems andmethods disclosed herein in which the grid density of the blood vesselmodel are determined based on the type of the target blood vessel maybetter show the feature information of the target blood vessel, therebyimproving the accuracy of the blood vessel parameter of the target bloodvessel determined based thereon.

In some embodiments, the processing device 120A may determine the griddensity of the blood vessel model based on the curvature of the targetblood vessel. A relatively large curvature of the target blood vesselmay correspond to a relatively small grid density of the blood vesselmodel of the target blood vessel. For example, the target blood vesselmay include a plurality of blood vessel segments with differentcurvatures. The processing device 120A may divide a part of the bloodvessel model corresponding to a blood vessel segment of a relativelylarge curvature using grids of one or more relatively small grid sizesthat correspond to a relatively high grid density. The processing device120A may divide a part of the blood vessel model corresponding to ablood vessel segment of a relatively small curvature using grids of oneor more relatively large grid sizes that correspond to a relatively lowgrid density. Accordingly, compared to a method of dividing blood vesselmodels corresponding to blood vessel segments of different curvatures ofa same grid density, the systems and methods disclosed herein in whichthe grid density of the blood vessel model are determined based on thecurvature of the target blood vessel may better show the featureinformation of a specific position (e.g., a curved position) of thetarget blood vessel, thereby improving the accuracy of the blood vesselparameter of the target blood vessel determined based thereon.

In some embodiments, the processing device 120A may determine the griddensity of the blood vessel model based on a combination of the type ofthe target blood vessel and the curvature of the target blood vessel.For example, the target blood vessel may include an aortic blood vesseland a coronary blood vessel. The aortic blood vessel may include aplurality of blood vessel segments of different curvatures. Theprocessing device 120A may divide a part of the blood vessel modelcorresponding to a blood vessel segment of the aortic blood vessel of arelatively small curvature using grids of a first grid size, resultingin a first grid density in the part of the blood vessel model. Theprocessing device 120A may divide a part of the blood vessel modelcorresponding to a blood vessel segment of the aortic blood vessel of arelatively large curvature using grids of a second grid size, resultingin a second grid density in the part of the blood vessel model. Theprocessing device 120A may divide a part of the blood vessel modelcorresponding to the coronary blood vessel using grids of a third gridsize, resulting in a third grid density in the part of the blood vesselmodel. At least two of the first grid size, the second grid size, andthe third grid size may be different. At least two of the first griddensity, the second grid density, and the third grid density may bedifferent. For instance, the first grid size may be greater than thesecond grid size, and the second grid size may be greater than the thirdgrid size; accordingly, the first grid density may be lower than thesecond grid density, and the second grid size may be lower than thethird grid density.

FIG. 7 is a schematic diagram illustrating an exemplary blood vesselmodel of a coronary artery according to some embodiments of the presentdisclosure. As illustrated in FIG. 7 , a blood vessel model 700 of acoronary artery of a patient may be divided into a plurality of grids.The blood vessel model 700 may be obtained by performing an imagesegmentation operation on a CTA image of the heart of the patient. Thegrid density of different regions of the blood vessel model 700 may bedifferent. For example, compared to a region 720 or a region 730corresponding to a main coronary artery, a region 710 corresponding to abranch of the coronary artery may be divided into a plurality of gridsof a smaller grid size. Compared to the region 720 of a relatively smallcurvature, the region 730 of a relatively large curvature may be dividedinto a plurality of grids of a smaller grid size.

Further, the processing device 120A may generate the blood vessel modelby performing a grid division on the blood vessel image based on the oneor more model parameters. As used herein, a grid division refers to aprocess of generating grids or meshes on a model. The grid division mayinclude a surface grid division, a volume grid division, or the like.The surface grid division may include dividing a surface of the modelinto 2-dimensional grids. That is, a surface grid model may be generatedby performing the surface grid division on the model, e.g., a model 1510shown in FIG. 15 . Algorithms used in the grid division may include atriangular grid division, a quadrilateral grid division, a hexagonalgrid division, or the like, or any combination thereof. Exemplary griddivision algorithms may include a Loop algorithm, a butterflysubdivision algorithm, a Catmull-Clark algorithm, a Doo-Sabin algorithm,a Delaunay triangular division algorithm, an advancing front algorithm,an Octree algorithm, an Elliptic algorithm, a parametric model basedalgorithm, a trans-finite Interpolation (TFI) algorithm, a partialdifferential equations (PDE) algorithm, a C type, 0 type, H type(C.O.H.) grid generation algorithm, a transformation extensionalgorithm, a covering algorithm, or the like. The volume grid divisionmay include dividing a model into 3-dimensional grids. The 3-dimensionalgrids may include a tetrahedral grid, a hexahedral grid, a prismaticgrid (i.e., a boundary layer grid), a mixture grid of tetrahedron andhexahedron, a Cartesian grid, a ball filling grid, or the like. That is,a volume grid model may be generated by performing the volume griddivision on the model, e.g., a model 1520 shown in FIG. 15 .

Merely by way of example, the processing device 120A may generate theblood vessel model by performing a grid division on the blood vesselimage based on the one or more model parameters according to a Delaunaytriangular division algorithm. By applying the Delaunay triangulardivision algorithm, a plurality of acute triangle grids of the bloodvessel model may be generated. For a 2D blood vessel model, none of theacute triangle grids may intersect with another acute triangle grid; twoneighboring acute triangle grids may share no more than one common edge.For a 3D blood vessel model, none of the acute triangle grids mayintersect with another acute triangle grid; two neighboring acutetriangle grids may share no more than one common face. As used herein,two (2D or 3D) grids are considered neighboring grids if there are noother grid between the two grids.

In some embodiments, the processing device 120A may perform a griddivision (e.g., a surface grid division) on a 2D blood vessel imagebased on the one or more model parameters, to generate a 2D blood vesselmodel. In some embodiments, the processing device 120A may perform agrid division (e.g., a surface grid division, a volume grid division) ona 3D blood vessel image based on the one or more model parameters, togenerate a 3D blood vessel model.

Additionally or alternatively, the processing device 120A may generatean initial blood vessel model of the target blood vessel based on theblood vessel image. For example, the processing device 120A may identifya centerline of the target blood vessel in the blood vessel imageaccording to one or more centerline determination algorithms. As usedherein, a centerline of a blood vessel refers to an imaginary linelocated in the blood vessel. In some embodiments, the centerline mayinclude a line of a set of one or more pixels (or voxels) in or near thecenter of the target blood vessel. Exemplary centerline determinationalgorithms may include a topological refinement algorithm, a trackingalgorithm, a shortest path algorithm, a distance transformationalgorithm, a similar region growth algorithm, or the like. Theprocessing device 120A may then determine a contour of the target bloodvessel based on the centerline of the target blood vessel. For example,the processing device 120A may determine a plurality of cross sectionsperpendicular to the centerline of the target blood vessel. For eachcross section of the plurality of cross sections, the processing device120A may determine a blood vessel region on the cross section based onthe blood vessel image corresponding to the cross section and one ormore cross sections adjacent to the cross section. The processing device120A may determine the contour of the target blood vessel based on aboundary of the blood vessel region on the each cross section of theplurality of cross sections. The processing device 120A may furtherreconstruct the initial blood vessel model of the target blood vesselbased on the centerline of the target blood vessel and the contour ofthe target blood vessel. For example, the processing device 120A mayreconstruct the initial blood vessel model of the target blood vesselbased on the centerline of the target blood vessel and the contour ofthe target blood vessel according to one or more reconstructionalgorithms. Further, the processing device 120A may perform a griddivision on the initial blood vessel model based on the one or moremodel parameters, to generate the blood vessel model. As anotherexample, the processing device 120A may generate a contour maprepresenting the blood vessel region based on a plurality of crosssections of the target blood vessel. The processing device 120A mayreconstruct the blood vessel model of the target blood vessel based onthe contour map according to one or more grid division algorithms (e.g.,a restricted Delaunay triangulation algorithm, a marching cubealgorithm). As still another example, the processing device 120A maydetermine a plurality of points on a contour of each of a plurality ofcross sections of the target blood vessel. The processing device 120Amay connect the plurality of points on the contours of the plurality ofcross sections based on an order of the plurality of points on the eachcross section and a relationship between points on adjacent crosssections. The processing device 120A may reconstruct the blood vesselmodel of the target blood vessel based on the plurality of connectedpoints.

In 530, the processing device 120A (e.g., the blood vessel parameterdetermination module 430) may determine, based at least on the bloodvessel model, at least one blood vessel parameter of the target bloodvessel.

In some embodiments, a blood vessel parameter may reflect a physicalstate of a region or a point of the target blood vessel. For example,the blood vessel parameters may include a hemodynamic parameter at aspecific region or a specific point of the target blood vessel (e.g., anexit position, an entrance position, a center point, a position of arelatively large curvature). The blood vessel parameters may include ablood pressure, a blood flow volume, a blood vessel wall shear stress, ablood vessel wall stress, a blood flow velocity, a blood flow direction,a blood flow resistance, a blood pressure differential between twopositions of the target blood vessel, a center line of the target bloodvessel, or the like, or any combination thereof. The blood vessel wallshear stress may be used to reflect a friction between a blood flow inthe target blood vessel and a vascular endothelium.

In some embodiments, the processing device 120A may obtain at least oneblood flow parameter of the target blood vessel. The processing device120A may then determine the at least one blood vessel parameter of thetarget blood vessel using a first trained model based on the bloodvessel model and the at least one blood flow parameter. For example, theprocessing device 120A may input the blood vessel model represented by aplurality of grids (and the at least one blood flow parameter) into thefirst trained model (e.g., a graph neural network model), and the firsttrained model may output the at least one blood vessel parametercorresponding to each grid node of a plurality of grid nodes (or eachgrid of a plurality of grids) of the blood vessel model. As used herein,a grid node of a blood vessel model refers to a point on the bloodvessel model. A grid may encompass one or more grid nodes. For example,a grid with a square shape may include four grid nodes at the fourcorners of the grid and one or more grid nodes within the grid. The atleast one blood vessel parameter corresponding to a grid node (or agrid) of the blood vessel model may indicate the at least one bloodvessel parameter at a point (or a region) of the target blood vesselcorresponding to the grid node or the grid on the blood vessel model.More descriptions for the determination of the at least one blood vesselparameter of the target blood vessel using a first trained model may befound elsewhere in the present disclosure (e.g., FIG. 6 and thedescriptions thereof).

In some embodiments, the processing device 120A may determine amathematical expression corresponding to the blood vessel model. Theprocessing device 120A may then determine the at least one blood vesselparameter of the target blood vessel using a second trained model basedon the mathematical expression. For example, the processing device 120Amay input the mathematical expression corresponding to the blood vesselmodel into the second trained model, and the second trained model mayoutput the at least one blood vessel parameter corresponding to eachgrid node of a plurality of grid nodes (or each grid of a plurality ofgrids) of the blood vessel model. More descriptions for thedetermination of the at least one blood vessel parameter of the targetblood vessel using a second trained model may be found elsewhere in thepresent disclosure (e.g., FIG. 12 and the descriptions thereof).

It should be noted that the above description regarding the process 500is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added inprocess 500. For example, process 500 may include an additionaloperation for transmitting the at least one blood vessel parameter to aterminal device (e.g., the terminal device 140) for display. As anotherexample, process 500 may include an additional operation fortransmitting the blood vessel model to a terminal device (e.g., theterminal device 140) for display. A user may modify a part or all of theblood vessel model. For example, the user may dilate, narrow, orsmoothen the blood vessel model, or a portion thereof.

FIG. 6 is a flowchart illustrating an exemplary process for determininga blood vessel parameter of a target blood vessel according to someembodiments of the present disclosure. In some embodiments, the process600 may be implemented in the medical system 100 illustrated in FIG. 1 .For example, the process 600 may be stored in the storage device 130and/or the storage (e.g., the storage 220, the storage 390) as a form ofinstructions, and invoked and/or executed by the processing device 120A(e.g., the processor 210 of the computing device 200 as illustrated inFIG. 2 , the CPU 340 of the mobile device 300 as illustrated in FIG. 3). The operations of the illustrated process presented below areintended to be illustrative. In some embodiments, the process 600 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 600 as illustrated inFIG. 6 and described below is not intended to be limiting. In someembodiments, at least part of operation 530 of process 500 may beperformed according to the process 600.

In 610, the processing device 120A (e.g., the obtaining module 410) mayobtain at least one blood flow parameter of a target blood vessel.

In some embodiments, the blood flow parameter of the target blood vesselmay be used to set a boundary condition of a blood vessel model of thetarget blood vessel. As used herein, a boundary condition refers to ablood flow condition at a boundary of a blood vessel model of a bloodvessel. The boundary may be an exit, an entrance, a vascular wall, orthe like, of the blood vessel (e.g., the target blood vessel).

The blood flow parameter may include a blood density, a blood viscosity,a blood flow velocity (e.g., an average blood flow velocity), a bloodflow volume (e.g., an average blood flow volume), a blood pressure, acardiac output, or the like, or any combination thereof. The blood flowparameter may be determined by one or more components (e.g., theprocessing device 120A, a blood flow detection device) of the medicalsystem 100, or manually set by a user of the medical system 100according to different situations. In some embodiments, the blood flowparameter of the target blood vessel may be determined based on featureinformation of the target blood vessel and/or feature information of anorgan or tissue that is connected to the target blood vessel. Forexample, the blood flow volume of a target blood vessel (e.g., acoronary artery entrance) may be estimated based on a cardiac outputand/or a diameter of the target blood vessel. The cardiac output isobtained by analyzing volume changes of a heart chamber in a cardiaccycle. In some embodiments, the blood flow parameter of the target bloodvessel may be determined based on empirical physiological data. Forexample, the blood flow volume of the target blood vessel (e.g., acoronary artery) may be proportional to the myocardial mass. Themyocardial mass may be obtained by a noninvasive technique, such as bymultiplying a myocardial volume to a myocardial density.

Additionally or alternatively, the blood flow parameter may be measuredby a blood flow detection device. The processing device 120A may obtainthe blood flow parameter from the blood flow detection device. Forexample, the blood pressure of the target blood vessel may be measuredby a blood-pressure meter. As another example, the blood flow velocityof the target blood vessel may be measured by a blood flow meter.

In 620, the processing device 120A (e.g., the blood vessel parameterdetermination module 430) may determine, based on a blood vessel modeland the at least one blood flow parameter, the at least one blood vesselparameter of the target blood vessel using a first trained model.

In some embodiments, the processing device 120A may preprocess the bloodvessel model based at least on the at least one blood flow parameter togenerate a preprocessed blood vessel model. For example, the processingdevice 120A may obtain at least one first grid node and at least onesecond grid node of the blood vessel model. In some embodiments, thefirst grid node may be a grid node with known blood flow parameters, andthe second grid node may be a grid node with unknown blood flowparameters. For example, the first grid node (also referred to as aboundary grid node) of a blood vessel model may include a grid node on aboundary of the blood vessel model, and the second grid node (alsoreferred to as a non-boundary grid node) may be a grid node on anon-boundary of the blood vessel model (e.g., a grid node inside theblood vessel model).

The processing device 120A may determine an initial value of the atleast one first grid node based on the at least one blood flowparameter. For example, the processing device 120A may determine a valueof a blood flow parameter measured at a specific position as an initialvalue of a first grid node corresponding to the specific position. Theprocessing device 120A may then determine an initial value of the atleast one second grid node based on a preset value. The preset value maybe determined by one or more components (e.g., the processing device120A) of the medical system 100, or manually set by a user of themedical system 100 according to different situations. For example, thepreset value may be 0. The processing device 120A may further generatethe preprocessed blood vessel model by preprocessing, based on theinitial value of the at least one first grid node and the initial valueof the at least one second grid node, the blood vessel model. Forexample, the processing device 120A may apply the initial value of theat least one first grid node and the initial value of the at least onesecond grid node to the blood vessel model.

Merely by way of example, the processing device 120A may generate ablood vessel model corresponding to a coronary artery of a patient. Theprocessing device 120A may determine a blood pressure measured at acoronary artery entrance as an initial value of at least one first gridnode corresponding to the coronary artery entrance of the blood vesselmodel. The processing device 120A may determine a blood flow volume atthe coronary artery entrance as an initial value of the at least onefirst grid node corresponding to the coronary artery entrance of theblood vessel model. The processing device 120A may determine an initialvalue of at least one second grid node corresponding to at least oneposition other than the coronary artery entrance as 0. The processingdevice 120A may generate a preprocessed blood vessel model by applyingthe initial value of the at least one first grid node and the initialvalue of the at least one second grid node to the blood vessel model.

Further, the processing device 120A may determine the at least one bloodvessel parameter of the target blood vessel based on the preprocessedblood vessel model using the first trained model. As used herein, afirst trained model refers to a model (e.g., a machine learning model)or an algorithm for determining at least one blood vessel parameter of atarget blood vessel based on a blood vessel model (or a preprocessedblood vessel model) of the target blood vessel. For example, the firsttrained model may be configured to generate the at least one bloodvessel parameter of the target blood vessel based on the blood vesselmodel (or the preprocessed blood vessel model) of the target bloodvessel. As another example, the first trained model may be configured togenerate the at least one blood vessel parameter of the target bloodvessel based on the blood vessel model (or the preprocessed blood vesselmodel) of the target blood vessel and at least one blood flow parameterof the target blood vessel.

In some embodiments, the first trained g model may be constructed basedon a graph neural network (GNN) (e.g., a graph neural network). As usedherein, a graph neural network model refers to a type of a neuralnetwork model that directly uses an image as an input; that is, thegraph neural network model may directly process image data. Merely byway of example, the processing device 120A may generate a blood vesselmodel of a target blood vessel and/or preprocess the blood vessel modelbased on at least one blood vessel parameter of the target blood vesselto generate a preprocessed blood vessel model. The processing device120A may input the preprocessed blood vessel model and the at least oneblood flow parameter into the first trained model (e.g., a trained GNNmodel). The first trained model (e.g., the trained GNN model) may outputat least one blood vessel parameter corresponding to a plurality of gridnodes of the preprocessed blood vessel model. The at least one bloodvessel parameter corresponding to the plurality of grid nodes of thepreprocessed blood vessel model may indicate the at least one bloodvessel parameter at a plurality of points of the target blood vesselcorresponding to the plurality of grid nodes of the preprocessed bloodvessel model.

In some embodiments, the processing device 120A may retrieve the firsttrained model from the storage device 130, the terminal device 140, orany other storage device. For example, the first trained model may beobtained by training a first preliminary model using a processing devicedifferent from or same as the processing device 120A. The first trainedmodel may be stored in the storage device 130, the terminal device 140,or any other storage device. The processing device 120A may retrieve thetrained machine learning model from the storage device 130, the terminaldevice 140, or any other storage device. More descriptions of thedetermination of the first trained model may be found elsewhere in thepresent disclosure (e.g., FIGS. 9-11 , and descriptions thereof).

According to some embodiments of the present disclosure, the at leastone blood vessel parameter of the target blood vessel may be determinedbased on the blood vessel model of the target blood vessel and the atleast one blood flow parameter of the target blood vessel using thefirst trained model. Compared with a blood vessel parameterdetermination approach that uses a computational fluid dynamics (CFD)algorithm to determine blood vessel parameters based on a blood vesselimage, the systems and methods as disclosed herein may simplify theblood vessel parameters determination process by inputting the bloodvessel model (and the at least one blood flow parameter) into the firsttrained model. Thus, the efficiency and/or accuracy of the blood vesselparameters determination may further be improved.

It should be noted that the above description regarding the process 600is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, an image format conversion operationmay be added in process 600. For example, an image format of thepreprocessed blood vessel model may be converted into an image formatthat meets an input condition of the first trained model. Merely by wayof example, the preprocessed blood vessel model with an enhancedmetafile (EMF) format may be converted into a joint photographic expertsgroup (JPED) format that meets the input condition of the first trainedmodel.

FIG. 8 is a schematic diagram illustrating an exemplary process fordetermining at least one blood vessel parameter of a target blood vesselaccording to some embodiments of the present disclosure.

As illustrated in FIG. 8 , in 810, the processing device 120A may obtaina blood vessel image of a target blood vessel as described in connectionwith operation 510. For example, the processing device 120A maydetermine a portion of an original image including the target bloodvessel as the blood vessel image. In 820, the processing device 120A maygenerate a blood vessel model of the target blood vessel based on theblood vessel image as described in connection with operation 520. In830, the processing device 120A may obtain at least one blood flowparameter of the target blood vessel as described in connection withoperation 610. The at least one blood flow parameter of the target bloodvessel may be determined based on the blood vessel image, or be measuredby a blood flow detection device.

In 840, the processing device 120A may obtain a first trained model asdescribed in connection with operation 620. In 850, the processingdevice 120A may determine the at least one blood vessel parameter of thetarget blood vessel based on the blood vessel model and the at least oneblood flow parameter using the first trained model as described inconnection with operation 620. For example, the processing device 120Amay generate a preprocessed blood vessel model by preprocessing theblood vessel model based at least on the at least one blood flowparameter. The processing device 120A may then determine the at leastone blood vessel parameter of the target blood vessel by inputting thepreprocessed blood vessel model into the first trained model.

FIG. 9 is a flowchart illustrating an exemplary process for generating afirst trained model according to some embodiments of the presentdisclosure. In some embodiments, the process 900 may be implemented inthe medical system 100 illustrated in FIG. 1 . For example, the process900 may be stored in the storage device 130 and/or the storage (e.g.,the storage 220, the storage 390) as a form of instructions, and invokedand/or executed by the processing device 120B (e.g., the processor 210of the computing device 200 as illustrated in FIG. 2 , the CPU 340 ofthe mobile device 300 as illustrated in FIG. 3 ). The operations of theillustrated process presented below are intended to be illustrative. Insome embodiments, the process 900 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 900 as illustrated in FIG. 9 and described below is notintended to be limiting.

In 910, the processing device 120B (e.g., the obtaining module 440) mayobtain a plurality of groups of first training samples.

In some embodiments, each group of the plurality of groups of firsttraining samples may include a preprocessed sample blood vessel model ofa sample blood vessel and at least one reference blood vessel parameterof the sample blood vessel. Each group of the plurality of groups offirst training samples may include a sample blood vessel image of thesample blood vessel and a sample original image of the sample bloodvessel. As used herein, a sample blood vessel refers to a blood vesselor a part thereof. The type of the sample blood vessel may be the sameas or different from the type of the target blood vessel. For example,the sample blood vessel and the target blood vessel may both be anaortic vessel. As another example, the sample blood vessel may be acoronary artery, and the target blood vessel may be an aortic vessel.

In some embodiments, the processing device 120B may obtain a sampleoriginal image of the sample blood vessel. The sample original image ofthe sample blood vessel may be a historical image generated by scanninga subject, or a part of the subject, using a medical device (e.g., themedical device 110, another medical device). The processing device 120Bmay determine a portion of the sample original image including thesample blood vessel as the sample blood vessel image. The determinationof the sample blood vessel image based on the sample original image maybe performed in a similar manner as the determination of a blood vesselimage based on an original image as described in connection withoperation 510 in FIG. 5 , the descriptions of which are not repeatedhere.

The processing device 120B may determine a sample blood vessel model ofthe sample blood vessel based on the sample blood vessel image. Forexample, the processing device 120B may determine at least one modelparameter of the sample blood vessel model based on feature informationof the sample blood vessel. The processing device 120B may generate thesample blood vessel model by performing, based on the at least one modelparameter, a grid division on the sample blood vessel image. Thegeneration of the sample blood vessel model based on the sample bloodvessel image may be performed in a similar manner as the generation of ablood vessel model based on a blood vessel image as described inconnection with operation 520 in FIG. 5 , the descriptions of which arenot repeated here.

The processing device 120B may obtain at least one sample blood flowparameter of the sample blood vessel. The sample blood flow parametermay include a sample blood density, a sample blood viscosity, a sampleblood flow velocity, a sample blood flow volume, a sample bloodpressure, a sample cardiac output, or the like, or any combinationthereof. In some embodiments, the sample blood flow parameter may bedetermined by one or more components (e.g., the processing device 1206,a blood flow detection device) of the medical system 100, or manuallyset by a user of the medical system 100 according to differentsituations. In some embodiments, the sample blood flow parameter of thesample blood vessel may be determined based on feature information ofthe sample blood vessel and/or feature information of an organ or tissuethat is connected to the sample blood vessel. Additionally oralternatively, the sample blood flow parameter may be measured by ablood flow detection device. The processing device 1206 may obtain thesample blood flow parameter from the blood flow detection device. Theobtaining of the at least one sample blood flow parameter of the sampleblood vessel may be performed in a similar manner as the obtaining of atleast one blood flow parameter of a target blood vessel as described inconnection with operation 610 in FIG. 6 , the descriptions of which arenot repeated here.

The processing device 120B may generate the preprocessed sample bloodvessel model by preprocessing, based on the at least one sample bloodflow parameter, the sample blood vessel model. For example, theprocessing device 120B may obtain at least one first sample grid nodeand at least one second sample grid node of the sample blood vesselmodel. The processing device 120B may determine an initial value of theat least one first sample grid node based on the at least one sampleblood flow parameter. The processing device 120B may determine aninitial value of the at least one second sample grid node based on apreset value. The processing device 120B may generate a preprocessedsample blood vessel model by preprocessing, based on the initial valueof the at least one first sample grid node and the initial value of theat least one second sample grid node, the sample blood vessel model. Thegeneration of the preprocessed sample blood vessel model based on thesample blood vessel model may be performed in a similar manner as thegeneration of a preprocessed blood vessel model based on a blood vesselmodel as described in connection with operation 620 in FIG. 6 , thedescriptions of which are not repeated here.

In some embodiments, the processing device 120B may determine the atleast one reference blood vessel parameter of the sample blood vesselbased on the sample blood vessel model and the at least one sample bloodflow parameter according to a first predetermined algorithm. Forexample, the at least one reference blood vessel parameter correspondingto each grid node of a plurality of grid nodes (or each grid of aplurality of grids) of the sample blood vessel model may be obtainedbased on the sample blood vessel model and the at least one sample bloodflow parameter according to the first predetermined algorithm. In someembodiments, the reference blood vessel parameter may include areference blood pressure, a reference blood flow volume, a referenceblood vessel wall shear stress, a reference blood vessel wall stress, areference blood flow velocity, a reference blood flow direction, areference blood flow resistance, a reference center line of the sampleblood vessel, or the like, or any combination thereof.

In some embodiments, the first predetermined algorithm may include acomputational fluid dynamics (CFD) algorithm. As used herein, a CFDrefers to an interdisciplinary method relating to mathematics, fluidmechanics, and computer science. The use of CFD may include simulatingand analyzing fluid mechanics problems by solving control equations offluid mechanics with computers and numerical methods. A control equationbased on Euler equations, Navier-Stokes equations, or a LatticeBoltzmann method may be used in the determination of the at least onereference blood vessel parameter. A discretization technique such as afinite difference technique, a finite volume technique, a finite elementtechnique, a boundary element technique, a spectral technique, a LatticeBoltzmann technique, a meshless technique, or the like, or anycombination thereof, may be used in the determination of the at leastone reference blood vessel parameter. A fluid of the flow fieldcomputation that used in the determination of the at least one referenceblood vessel parameter may be viscous or non-viscous. The fluid may becompressible or incompressible. The fluid may be a laminar flow or aturbulent flow. The fluid may be a steady flow or an unsteady flow. Acorresponding control equation or simulation method may be selectedbased on physical features of the simulated fluid. For example, theEuler equations or the Lattice Boltzmann method may be selected for theflow field computation of the non-viscous fluid, while the Navier-Stokesequations or the Lattice Boltzmann method may be selected for the flowfield computation of the viscous fluid. For illustration purposes, acomputation of the CFD for a coronary artery may use the Navier-Stokesequations.

In 920, the processing device 120B (e.g., the trained model generationmodule 450) may generate a first trained model by training a firstpreliminary model using the plurality of groups of first trainingsamples.

As used herein, a preliminary model refers to a machine learning modelto be trained. In some embodiments, the preliminary model may include aninput layer an output layer, and a plurality of hidden layers. As usedherein, a layer of a model may refer to an algorithm or a function forprocessing input data of the layer. Different layers may preformdifferent kinds of processing on their respective input. For example,the input layer may be configured to receive an input of the preliminarymodel. Each hidden layer may perform a specific function, e.g.,convolution, pooling, normalization, matrix multiplication, non-linearactivation, or the like. The output layer may receive an input from thepreceding layer and apply one or more transformations to the receivedinput to generate a processing result of the preliminary model. In someembodiments, each of the layer may include one or more nodes. In someembodiments, each node may be connected to one or more nodes in aprevious layer. The number of nodes in each layer may be the same ordifferent. In some embodiments, each node may correspond to anactivation function. As used herein, an activation function of a nodemay define an output of the node given an input or a set of inputs. Insome embodiments, the processing device 120B may initialize one or moreparameter values of one or more first parameters in the firstpreliminary model. Exemplary first parameters in the first preliminarymodel may include a size of a kernel of a layer, a total count (ornumber) of layers, a count (or number) of nodes in each layer, alearning rate, a batch size, an epoch, a connected weight between twoconnected nodes, a bias vector relating to a node, or the like. In someembodiments, the initial values of the first parameters may be defaultvalues determined by the medical system 100 or preset by a user of themedical system 100. In some embodiments, the processing device 120B mayobtain the first preliminary model from a storage device (e.g., thestorage device 130) of the medical system 100 and/or an external storagedevice via the network 150.

In some embodiments, the first trained model may be determined bytraining the first preliminary model using the plurality of groups offirst training samples. One or more parameter values of the plurality offirst parameters may be altered during the training of the firstpreliminary model using the plurality of groups of first trainingsamples. In some embodiments, the first preliminary model may be trainedbased on the plurality of groups of first training samples using atraining algorithm. Exemplary training algorithms may include a gradientdescent algorithm, a Newton's algorithm, a Quasi-Newton algorithm, aLevenberg-Marquardt algorithm, a conjugate gradient algorithm, agenerative adversarial learning algorithm, or the like.

In some embodiments, the first trained model may be determined byperforming a plurality of iterations to iteratively update one or moreparameter values of the plurality of first parameters of the firstpreliminary model. For each of the plurality of iterations, a specificgroup of first training samples may first be input into the firstpreliminary model. For example, a specific preprocessed sample bloodvessel model in a specific group of first training samples may beinputted into an input layer of the first preliminary model, and areference blood vessel parameter corresponding to the specificpreprocessed sample blood vessel model may be inputted into an outputlayer of the first preliminary model as a desired output of the firstpreliminary model. The first preliminary model may extract one or moreimage features (e.g., a low-level feature (e.g., an edge feature, atexture feature), a high-level feature (e.g., a semantic feature), or acomplicated feature (e.g., a deep hierarchical feature) included in thespecific group of first training samples. Based on the extracted imagefeatures, the first preliminary model may determine a predicted output(i.e., a sample blood vessel parameter) of the specific group of firsttraining samples. The predicted output (i.e., the sample blood vesselparameter) of the specific group of first training samples may then becompared with the reference blood vessel parameter of the specific groupof first training samples based on a cost function. As used herein, acost function of a machine learning model may be configured to assess adifference between a predicted output (e.g., a sample blood vesselparameter) of the machine learning model and a desired output (e.g., areference blood vessel parameter). If the value of the cost functionexceeds a threshold in a current iteration, parameter values of thefirst preliminary model may be adjusted and/or updated in order todecrease the value of the cost function (i.e., the difference betweenthe sample blood vessel parameter and the reference blood vesselparameter) to smaller than the threshold, and an intermediate model maybe generated. Accordingly, in the next iteration, another group of firsttraining samples may be input into the intermediate model to train theintermediate model as described above.

The plurality of iterations may be performed to update the parametervalues of the first preliminary model (or the intermediate model) untila termination condition is satisfied. The termination condition mayprovide an indication of whether the first preliminary model (or theintermediate model) is sufficiently trained. If the terminationcondition is satisfied in a current iteration, the processing device120B may designate the first preliminary model (or the intermediatemodel) obtained in the current iteration as the first trained model. Thetermination condition may relate to the cost function or an iterationcount of the iterative process or training process. For example, thetermination condition may be satisfied if the value of the cost functionassociated with the first preliminary model (or the intermediate model)is minimal or smaller than a threshold (e.g., a constant). As anotherexample, the termination condition may be satisfied if the value of thecost function converges. The convergence may be deemed to have occurredif the variation of the values of the cost function in two or moreconsecutive iterations is smaller than a threshold (e.g., a constant).As still another example, the termination condition may be satisfiedwhen a specified number (or count) of iterations are performed in thetraining process.

It should be noted that, in response to a determination that the valueof the cost function associated with the first preliminary model (or theintermediate model) is equal to the threshold (e.g., the constant), theprocessing device 120B may either determine that the terminationcondition is satisfied or determine that the termination condition isnot satisfied.

It should be noted that the above description regarding the process 900is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, an image format conversion operationmay be added in process 900. For example, an image format of the samplepreprocessed blood vessel model may be converted into an image formatthat meets an input condition of the first preliminary model.

FIG. 10 is a schematic diagram illustrating an exemplary first trainedmodel according to some embodiments of the present disclosure.

As illustrated in FIG. 10 , a first trained model 1000 is a graph U-Netconstructed based on a graph convolutional network. The first trainedmodel 1000 includes an encoder 1020 and a decoder 1040. The encoder 1020may be a graph convolutional network configured to determine aconvolution of an input (e.g., a blood vessel model, a preprocessedblood vessel model) of the first trained model 1000, and encode theconvolution to generate an encoding result 1030. The decoder 1040 may bea graph inverse convolutional neural network configured to decode theencoding result 1030, and generate an output (e.g., a blood vessel modelincluding blood vessel parameters).

FIG. 11 is a schematic diagram illustrating an exemplary process forgenerating a first trained model according to some embodiments of thepresent disclosure.

As illustrated in FIG. 11 , in 1110, the processing device 120B mayobtain a sample blood vessel image of a sample blood vessel as describedin connection with operation 910. For example, the processing device120B may determine a portion of a sample original image including thesample blood vessel as the sample blood vessel image. In 1120, theprocessing device 1206 may determine a sample blood vessel model of thesample blood vessel based on the sample blood vessel image as describedin connection with operation 910. For example, the processing device120B may determine at least one model parameter of the sample bloodvessel model based on feature information of the sample blood vessel.The processing device 120B may generate the sample blood vessel model byperforming, based on the at least one model parameter, a grid divisionon the sample blood vessel image.

In 1130, the processing device 1206 may obtain at least one sample bloodflow parameter of the sample blood vessel as described in connectionwith operation 910. The at least one sample blood flow parameter of thesample blood vessel may be determined based on the sample blood vesselimage, or be measured by a blood flow detection device. In 1140, theprocessing device 1206 may determine the at least one reference bloodvessel parameter of the sample blood vessel based on the sample bloodvessel model and the at least one sample blood flow parameter asdescribed in connection with operation 910. For example, the processingdevice 1206 may determine the at least one reference blood vesselparameter of the sample blood vessel based on the sample blood vesselmodel and the at least one sample blood flow parameter according to acomputational fluid dynamics algorithm.

In 1150, the processing device 120B may generate a preprocessed sampleblood vessel model by preprocessing the sample blood vessel model basedon at least one sample blood flow parameter as described in connectionwith operation 910. In 1160, the processing device 1206 may generate thefirst trained model by training a first preliminary model based on thepreprocessed sample blood vessel model and the at least one referenceblood vessel parameter of the sample blood vessel as described inconnection with operation 920.

FIG. 12 is a flowchart illustrating an exemplary process for determininga blood vessel parameter of a target blood vessel according to someembodiments of the present disclosure. In some embodiments, the process1200 may be implemented in the medical system 100 illustrated in FIG. 1. For example, the process 1200 may be stored in the storage device 130and/or the storage (e.g., the storage 220, the storage 390) as a form ofinstructions, and invoked and/or executed by the processing device 120A(e.g., the processor 210 of the computing device 200 as illustrated inFIG. 2 , the CPU 340 of the mobile device 300 as illustrated in FIG. 3). The operations of the illustrated process presented below areintended to be illustrative. In some embodiments, the process 1200 maybe accomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 1200 as illustrated inFIG. 12 and described below is not intended to be limiting. In someembodiments, at least part of operation 530 of process 500 may beperformed according to process 1200.

In 1210, the processing device 120A (e.g., the blood vessel parameterdetermination module 430) may determine a mathematical expressioncorresponding to a blood vessel model.

The mathematical expression corresponding to the blood vessel model maybe used to describe properties or characteristics of a plurality ofgrids of the blood vessel model in numerically. The mathematicalexpression may include an array, a vector, a vector matrix, or the like,or any combination thereof.

In some embodiments, the processing device 120A may determine themathematical expression corresponding to the blood vessel model bynumerically processing the blood vessel model. For example, theprocessing device 120A may determine the mathematical expressioncorresponding to the blood vessel model based on coordinate informationof a plurality of grids of the blood vessel model and the type of theblood vessel model.

In some embodiments, the blood vessel model may be a structured gridmodel (e.g., a structured surface grid model, a structured volume gridmodel), and the processing device 120A may divide the structured gridmodel into at least one first layer along an axial direction of theblood vessel model. Further, the processing device 120A may determinethe mathematical expression based on first coordinate information of aplurality of first grids of the at least one first layer. Moredescriptions for determining a mathematical expression corresponding toa structured grid model may be found elsewhere in the present disclosure(e.g., FIG. 13 , and the descriptions thereof).

In some embodiments, the blood vessel model may be an unstructured gridmodel, and the processing device 120A may map the unstructured gridmodel to a structured grid model. For example, the processing device120A may determine a key element group and a local direction groupcorresponding to each grid of a plurality of grids in an unstructuredgrid model. The key element group corresponding to a grid may include aplurality of edges and at least one vertex that can determine a localdirection of the grid. Merely by way of example, a key element group ofa triangular grid may include three edges and one vertex. In someembodiments, the processing device 120A may determine a key element anda local direction corresponding to the each grid of the plurality ofgrids based on the key element group and the local direction groupcorresponding to the grid using, e.g., a maximized Jacobian matrix anddeterminant or a front propulsion technology. The processing device 120Amay transform the unstructured grid model to a structured grid modelbased on the key element and the local direction corresponding to theeach grid. The processing device 120A may then determine a mathematicalexpression corresponding to the structured grid model. For example, theprocessing device 120A may divide the structured grid model into atleast one second layer along an axial direction of the structured gridmodel. The processing device 120A may determine the mathematicalexpression based on second coordinate information of a plurality ofsecond girds of the at least one second layer. The processing device120A may further designate the mathematical expression corresponding tothe structured grid model as the mathematical expression correspondingto the unstructured grid model. Additionally or alternatively, theprocessing device 120A may determine the mathematical expression basedon third coordinate information of a plurality of third girds of theunstructured grid model.

In 1220, the processing device 120A (e.g., the blood vessel parameterdetermination module 430) may determine, based on the mathematicalexpression, at least one blood vessel parameter of a target blood vesselusing a second trained model.

As used herein, a second trained model refers to a model (e.g., amachine learning model) or an algorithm for determining at least oneblood vessel parameter of a target blood vessel based on a mathematicalexpression corresponding to a blood vessel model of the target bloodvessel. In some embodiments, the second trained model may be constructedbased on a neural network model, such as a deep neural network (DNN)model, a convolutional neural network model (CNN) model, a recurrentneural network (RNN) model, a feature pyramid network (FPN) model, afully convolutional neural network (FCN) model, a generative adversarialnetwork (GAN) model, or the like, or any combination thereof. Forexample, the processing device 120A may input the mathematicalexpression corresponding to a blood vessel model of the target bloodvessel into the second trained model. The second trained model mayoutput the at least one blood vessel parameter of the target bloodvessel. As another example, the processing device 120A may obtain atleast one blood flow parameter of the target blood vessel, and input themathematical expression and the at least one blood flow parameter intothe second trained model. The second trained model may output the atleast one blood vessel parameter of the target blood vessel.

In some embodiments, the processing device 120A may retrieve the secondtrained model from the storage device 130, the terminal device 140, orany other storage device. For example, the second trained model may beobtained by training a second preliminary model using a processingdevice different from or the same as the processing device 120A. Thesecond trained model may be stored in the storage device 130, theterminal device 140, or any other storage device. The processing device120A may retrieve the second trained model from the storage device 130,the terminal device 140, or any other storage device. More descriptionsof the determination of the second trained model may be found elsewherein the present disclosure (e.g., FIGS. 18-19 , and the descriptionsthereof).

According to some embodiments of the present disclosure, the at leastone blood vessel parameter of the target blood vessel may be determinedbased on the mathematical expression corresponding to the blood vesselmodel of the target blood vessel and the at least one blood flowparameter of the target blood vessel using the second trained model.Compared with an approach which only uses the mathematical expressioncorresponding to the blood vessel model of the target blood vessel todetermine blood vessel parameters, the systems and methods disclosedherein may improve the accuracy of the blood vessel parametersdetermination by using both the mathematical expression corresponding tothe blood vessel model of the target blood vessel and the at least oneblood flow parameter of the target blood vessel. In addition, comparedwith a blood vessel parameter determination approach that uses acomputational fluid dynamics (CFD) algorithm to determine blood vesselparameters based on a blood vessel image, the systems and methods for asdisclosed herein may simplify the blood vessel parameters determinationprocess by inputting the mathematical expression corresponding to theblood vessel model of the target blood vessel (and the at least oneblood flow parameter of the target blood vessel) into the second trainedmodel. Thus, the efficiency and/or accuracy of the blood vesselparameters determination may further be improved.

Furthermore, a surface grid model of the target blood vessel may begenerated based on the blood vessel image of the target blood vessel.Compared with a volume grid model, the surface grid model may use fewergrids to represent the geometry of target blood vessel, and themathematical expression of the surface grid model may be simpler thanthat of the volume grid model. When the mathematical expression of thesurface grid model is determined as an input of the second trainedmodel, the number (or count) of parameters used for training of thesecond trained model may be reduced, which may improve the trainingefficiency of the second trained model and reduce the memory occupied.

It should be noted that the above description regarding the process 1200is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 13 is a flowchart illustrating an exemplary process for determininga mathematical expression corresponding to a structured grid modelaccording to some embodiments of the present disclosure. In someembodiments, the process 1300 may be implemented in the medical system100 illustrated in FIG. 1 . For example, the process 1300 may be storedin the storage device 130 and/or the storage (e.g., the storage 220, thestorage 390) as a form of instructions, and invoked and/or executed bythe processing device 120A (e.g., the processor 210 of the computingdevice 200 as illustrated in FIG. 2 , the CPU 340 of the mobile device300 as illustrated in FIG. 3 ). The operations of the illustratedprocess presented below are intended to be illustrative. In someembodiments, the process 1300 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 1300 as illustrated in FIG. 13 and described below is notintended to be limiting. In some embodiments, at least part of operation1210 of process 1200 may be performed according to process 1300.

In 1310, the processing device 120A (e.g., the blood vessel parameterdetermination module 430) may divide a structured grid model into atleast one layer along an axial direction of the structured grid model.

As used herein, an axial direction of a blood vessel model refers to adirection parallel to a center line of the blood vessel model. Forexample, the axial direction may be a direction along the center line ofthe blood vessel model. The layer may be a cross-section of the bloodvessel model perpendicular to the axial direction of the blood vesselmodel. Each layer of the at least one layer may include a plurality ofgrids. For example, if the blood vessel model is a structured surfacegrid model, the layer may include a plurality of grids on a contour ofthe layer. As another example, if the blood vessel model is a structuredvolume grid model, the layer may include a plurality of grids on acontour and an interior of the layer. See, e.g., FIGS. 17A and 17B, andthe descriptions thereof.

In some embodiments, a position of a grid in the blood vessel model maybe represented by coordinates (e.g., 3D floating point coordinates) in amodel coordinate system. As used herein, coordinates of a grid in amodel coordinate system refers to coordinates of a specific point (e.g.,a center point) in the grid in the model coordinate system. In someembodiments, different model coordinate systems may be constructed fordifferent blood vessel models. For example, for the model coordinatesystem of the blood vessel model in some embodiments of the presentdisclosure, an origin may be a center point of the blood vessel model.The X-axis (e.g., the X-axis illustrated in FIG. 17A) may be from a leftside to a right side of the blood vessel model viewed from the directionof the blood flow within the blood vessel represented by the bloodvessel model. The Y-axis (e.g., the Y-axis illustrated in FIG. 17A) maybe along the direction of the blood flow within the blood vesselrepresented by the blood vessel model. The Z-axis (e.g., the Z-axisillustrated in FIG. 17A) direction may be perpendicular to the planedefined by the X-axis and the Y-axis.

In 1320, the processing device 120A (e.g., the blood vessel parameterdetermination module 430) may determine a mathematical expressioncorresponding to the structured grid model based on coordinateinformation of a plurality of grids of the at least one layer.

In some embodiments, the blood vessel model may be a structured surfacegrid model, and each layer of the at least one layer of the structuredsurface grid model may include a plurality of grids on a contour of thelayer. For example, if the cross-section of the blood vessel model iscircular, the plurality of grids may form a ring centered on a locationof a center of the layer. For each of the at least one layer, theprocessing device 120A may determine a layer vector corresponding to thelayer based on the coordinate information of the plurality of grids ofthe layer. In some embodiments, the plurality of grids of the layer maybe arranged in a row or a column. For example, a specific grid (e.g., anarbitrary grid) of the layer may be designated as a first grid of a rowof grids, a grid next to the first grid in a clockwise direction (or acounterclockwise direction) may be designated as a second grid of therow of grids, a grid next to the second grid in the clockwise direction(or the counterclockwise direction) may be designated as a third grid ofthe row of grids, and so on. The processing device 120A may thendetermine the layer vector corresponding to the layer based on thecoordinate information of the row of grids or the column of grids.Further, the processing device 120A may determine a vector matrix (e.g.,a 2D row vector matrix, a 2D column vector matrix) corresponding to theblood vessel model based on at least one layer vector corresponding tothe at least one layer. For example, each layer vector may be designatedas a row vector or a column vector of the vector matrix.

FIG. 17A is a schematic diagram illustrating an exemplary process fordetermining a mathematical expression corresponding to a structuredsurface grid model according to some embodiments of the presentdisclosure. As illustrated in FIG. 17A, the processing device 120A maydivide a structured surface grid model 1710 into a plurality of layers,e.g., a first layer A, a second layer B, a third layer C, . . . , and anth layer N, along an axial direction D of the structured surface gridmodel 1710. Each layer of the plurality of layers may include aplurality of grids. For example, the first layer A may include aplurality of first grids Ga, the second layer B may include a pluralityof second grids Gb, the third layer C may include a plurality of thirdgrids Gc, . . . , and the nth layer may include a plurality of nth gridsGn. The plurality of grids of the each layer may be arranged in a row.For example, a specific first grid (e.g., an arbitrary first grid) ofthe first layer A may be designated as a first grid Ga1, a first gridnext to the first grid Ga1 in a clockwise direction (or acounterclockwise direction) may be designated as a first grid Ga2, afirst grid next to the first grid Ga2 in the clockwise direction (or thecounterclockwise direction) may be designated as a first grid Ga3, andso on.

The processing device 120A may determine a first layer vector Vacorresponding to the first layer A based on coordinate information ofthe plurality of first grids Ga of the first layer A. Merely by way ofexample, if coordinates of the first grid Ga1 are (X1, Y1, Z1),coordinates of the first grid Ga2 are (X2, Y2, Z2), coordinates of thefirst grid Ga3 are (X3, Y3, Z3), . . . , and coordinates of a first gridGan are (Xn, Yn, Zn), the processing device 120A may determine that thefirst layer vector Va corresponding to the first layer A is ((X1, Y1,Z1), (X2, Y2, Z2), (X3, Y3, Z3), . . . , (Xn, Yn, Zn)). Similarly, theprocessing device 120A may determine a second layer vector Vbcorresponding to the second layer B based on coordinate information ofthe plurality of second grids Gb of the second layer B. The processingdevice 120A may determine a third layer vector Vc corresponding to thethird layer C based on coordinate information of the plurality of thirdgrids Gc of the third layer C. The processing device 120A may determinea nth layer vector Vn corresponding to the nth layer N based oncoordinate information of the plurality of nth grids Gn of the nth layerN. Further, the processing device 120A may determine a vector matrixcorresponding to the structured surface grid model 1710 based on thefirst layer vector Va, the second layer vector Vb, the third layervector Vc, . . . , and the nth layer vector Vn. For example, theprocessing device 120A may determine that the vector matrixcorresponding to the structured surface grid model 1710 is (Va, Vb, Vc,Vn).

In some embodiments, the blood vessel model may be a structured volumegrid model, and each layer of the at least one layer of the structuredvolume grid model may include a plurality of grids on a contour and aninterior of the layer. The plurality of grids may form a plurality ofrings from a center of the layer and outward. In some embodiments, theplurality of rings of grids may be arranged in a plurality of rows ofgrids (or a plurality of columns of grids). Each of the plurality ofrings may correspond to each of the plurality of rows (or each of theplurality of columns). For example, the rows from top to bottom maycorrespond to the rings from the center outward in the layer,respectively. For each row of the plurality of rows in each layer, theprocessing device 120A may determine a row vector corresponding to therow based on the coordinate information of the plurality of grids of therow. For each layer of the at least one layer, the processing device120A may determine a layer vector matrix (e.g., a 2D layer vectormatrix) corresponding to the layer based on a plurality of row vectorscorresponding to the plurality of rows in the layer. Further, theprocessing device 120A may determine a vector matrix (e.g., a 3D vectormatrix) corresponding to the blood vessel model based on at least onelayer vector matrix corresponding to the at least one layer.

In some embodiments, for a structured volume grid model, the processingdevice 120A may determine whether a count of elements in each row vectorin each layer is the same. An element corresponds to a grid. In responseto determining that a count of elements in a row vector in a specificlayer is different from other row vectors in the specific layer, theprocessing device 120A may complement the row vector by adding elementssuch that the count of elements in the each row vector in each layer isthe same. For example, the processing device 120A may complement aspecific row vector by adding elements based on a grid relationshipbetween the specific row and a row adjacent to the specific row of thelayer. Accordingly, the count of elements in the each row vector in theeach layer may be the same, and a vector matrix may be determined basedon the plurality of row vectors, which may facilitate to perform aconvolution operation on the vector matrix.

Merely by way of example, in a structured volume grid model, a specificlayer may include a first row of grids, a second row of grids, and athird row of grids. The first row of grids may include a grid A1, a gridA2, and a grid A3. The second row of grids may include a grid B1 and agrid B2. The grid B1 is adjacent to the grid A1 and the grid A2, and thegrid B2 is adjacent to the grid A2 and the grid A3. The third row ofgrids may include a grid C1. The grid C1 is adjacent to the grid B1 andthe grid B2. A first row vector corresponding to the first row may bedetermined based on coordinate information of the grid A1, the grid A2,and the grid A3. The first element in the first row vector may becoordinates of the grid A1, the second element in the first row vectormay be coordinates of the grid A2, and the third element in the firstrow vector may be coordinates of the grid A3. That is, the count ofelements in the first row vector may be three. A second row vectorcorresponding to the second row may be determined based on coordinateinformation of the grid B1 and the grid B2. The first element in thesecond row vector may be coordinates of the grid B1, and the secondelement in the second row vector may be coordinates of the grid B2. Thatis, the count of elements in the second row vector may be two. A thirdrow vector corresponding to the third row may be determined based oncoordinate information of the grid C1. The element in the third rowvector may be coordinates of the grid C1. That is, the count of elementsin the third row vector may be one. The processing device 120A may thencomplement the second row vector and the third row vector by addingelements such that the count of elements in the each row vector in thespecific layer is the same. Merely by way of example, the processingdevice 120A may add an element whose value is the coordinates of thegrid B2 in the second row vector. The processing device 120A may add twoelements whose values of the coordinates of the grid C1 as the secondelement and the third element in the third row vector. Accordingly, thecount of elements in the each row vector in the specific layer may bethree.

FIG. 17B is a schematic diagram illustrating an exemplary process fordetermining a mathematical expression corresponding to a structuredvolume grid model according to some embodiments of the presentdisclosure. As illustrated in FIG. 17B, the processing device 120A maydivide a structured volume grid model 1720 into a plurality of layers,e.g., a first layer A, a second layer B, a third layer C, . . . , and anth layer N, along an axial direction D of the structured volume gridmodel 1720. Each layer of the plurality of layers may include aplurality of rows of grids. For example, the first layer A may include aplurality of rows of grids Ga (e.g., a row Ra1, a row Ra2, . . . , a rowRan), the second layer B may include a plurality of rows of grids Gb,the third layer C may include a plurality of rows of grids Gc, . . . ,and the nth layer may include a plurality of rows of grids Gn.

For the first layer A, the processing device 120A may determine a rowvector Va1 corresponding to the row Ra1 based on coordinate informationof the plurality of grids Ga of the row Ra1, a row vector Va2corresponding to the row Ra2 based on coordinate information of theplurality of grids Ga of the row Ra2, . . . , and a row vector Vancorresponding to the row Ran based on coordinate information of theplurality of grids Ga of the row Ran. Merely by way of example withreference to the determination of the row vector Va1 corresponding tothe row Ra1, if coordinates of a first grid of the row Ra1 are (X1, Y1,Z1), coordinates of a second grid of the row Ra1 are (X2, Y2, Z2),coordinates of a third grid of the row Ra1 are (X3, Y3, Z3), . . . , andcoordinates of a nth grid of the row Ra1 are (Xn, Yn, Zn), theprocessing device 120A may determine that the row vector Va1corresponding to the row Ra1 is ((X1, Y1, Z1), (X2, Y2, Z2), (X3, Y3,Z3), . . . , (Xn, Yn, Zn)). The processing device 120A may thendetermine a layer vector matrix Va corresponding to the first layer Abased on the row vector Va1, the row vector Va2, . . . , and the rowvector Van. Merely by way of example, the processing device 120A maydetermine that the layer vector matrix Va corresponding to the firstlayer A is (Va1, Va2, . . . , Van). Similarly, the processing device120A may determine a layer vector matrix Vb corresponding to the secondlayer B, a layer vector matrix Vc corresponding to the third layer C,and a layer vector matrix Vn corresponding to the nth layer N. Further,the processing device 120A may determine a vector matrix correspondingto the structured volume grid model 1720 based on the layer vectormatrix Va, the layer vector matrix Vb, the layer vector matrix Vc, . . ., and the layer vector matrix Vn. Merely by way of example, theprocessing device 120A may determine that vector matrix corresponding tothe structured volume grid model 1720 is (Va, Vb, Vc, Vn).

According to some embodiments of the present disclosure, a mathematicalexpression (e.g., an array, a vector, a vector matrix) corresponding toa blood vessel model may be determined based on coordinate informationof a plurality of grids of the blood vessel model. Compared with anapproach that uses pixel values of grids of a blood vessel model todetermine a mathematical expression corresponding to the blood vesselmodel, the systems and methods disclosed herein may improve the accuracyof the mathematical expression corresponding to the blood vessel modelby using the coordinate information of the plurality of grids of theblood vessel model, which may further improve the accuracy of the bloodvessel parameter of the target blood vessel determined based thereon.

It should be noted that the above description regarding the process 1300is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 18 is a flowchart illustrating an exemplary process for generatinga second trained model according to some embodiments of the presentdisclosure. In some embodiments, the process 1800 may be implemented inthe medical system 100 illustrated in FIG. 1 . For example, the process1800 may be stored in the storage device 130 and/or the storage (e.g.,the storage 220, the storage 390) as a form of instructions, and invokedand/or executed by the processing device 120B (e.g., the processor 210of the computing device 200 as illustrated in FIG. 2 , the CPU 340 ofthe mobile device 300 as illustrated in FIG. 3 ). The operations of theillustrated process presented below are intended to be illustrative. Insome embodiments, the process 1800 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 1800 as illustrated in FIG. 18 and described below is notintended to be limiting.

In 1810, the processing device 120B (e.g., the obtaining module 440) mayobtain a plurality of groups of second training samples.

In some embodiments, each group of the plurality of groups of secondtraining samples may include a sample mathematical expressioncorresponding to a sample blood vessel model of a sample blood vesseland at least one reference blood vessel parameter of the sample bloodvessel. For example, the processing device 1206 may obtain a sampleblood vessel image of the sample blood vessel. The processing device1206 may then determine, based on the sample blood vessel image, asample blood vessel model of the sample blood vessel. The processingdevice 1206 may further determine a sample mathematical expressioncorresponding to the sample blood vessel model. The determination of thesample mathematical expression corresponding to the sample blood vesselmodel may be performed in a similar manner as the determination of amathematical expression corresponding to a blood vessel model asdescribed in connection with operation 1210 in FIG. 12 , operations 1310and 1320 in FIG. 13 , the descriptions of which are not repeated here.

In some embodiments, the processing device 120B may determine the atleast one reference blood vessel parameter of the sample blood vesselbased on the sample blood vessel model and at least one sample bloodflow parameter of the sample blood vessel according to a secondpredetermined algorithm. In some embodiments, the second predeterminedalgorithm includes a computational fluid dynamics algorithm. Moredescriptions of the determination of the at least one reference bloodvessel parameter of the sample blood vessel may be found elsewhere inthe present disclosure (e.g., operation 910 in FIG. 9 , and thedescriptions thereof).

In 1820, the processing device 120B (e.g., the trained model generationmodule 450) may generate a second trained model by training a secondpreliminary model using the plurality of groups of second trainingsamples.

In some embodiments, the processing device 120B may initialize one ormore parameter values of one or more second parameters in the secondpreliminary model. Exemplary second parameters in the second preliminarymodel may include a size of a kernel of a layer, a total count (ornumber) of layers, a count (or number) of nodes in each layer, alearning rate, a batch size, an epoch, a connected weight between twoconnected nodes, a bias vector relating to a node, or the like. In someembodiments, the initialized values of the second parameters may bedefault values determined by the medical system 100 or preset by a userof the medical system 100. In some embodiments, the processing device1206 may obtain the second preliminary model from a storage device(e.g., the storage device 130) of the medical system 100 and/or anexternal storage device via the network 150.

In some embodiments, the second trained model may be determined bytraining second preliminary model using the plurality of groups ofsecond training samples. One or more parameter values of the pluralityof second parameters may be altered during the training of the secondpreliminary model using the plurality of groups of second trainingsamples. In some embodiments, the second preliminary model may betrained based on the plurality of groups of second training samplesusing a training algorithm. Exemplary training algorithms may include agradient descent algorithm, a Newton's algorithm, a Quasi-Newtonalgorithm, a Levenberg-Marquardt algorithm, a conjugate gradientalgorithm, a generative adversarial learning algorithm, or the like.

In some embodiments, the second trained model may be determined byperforming a plurality of iterations to iteratively update one or moreparameter values of the plurality of second parameters of the secondpreliminary model. For each of the plurality of iterations, a specificgroup of second training samples may first be input into the secondpreliminary model. For example, a specific sample mathematicalexpression corresponding to a sample blood vessel model in a specificgroup of second training samples may be inputted into an input layer ofthe second preliminary model, and a reference blood vessel parametercorresponding to the specific sample mathematical expression may beinputted into an output layer of the second preliminary model as adesired output of the second preliminary model. The second preliminarymodel may determine a predicted output (i.e., a sample blood vesselparameter) of the specific group of second training samples. Thepredicted output (i.e., the sample blood vessel parameter) of thespecific group of second training samples may then be compared with thereference blood vessel parameter of the specific group of secondtraining samples based on a cost function. If a value of the costfunction exceeds a threshold in a current iteration, parameter values ofthe second preliminary model may be adjusted and/or updated in order todecrease the value of the cost function (i.e., the difference betweenthe sample blood vessel parameter and the reference blood vesselparameter) to smaller than the threshold, and an intermediate model maybe generated. Accordingly, in the next iteration, another group ofsecond training samples may be input into the intermediate model totrain the intermediate model as described above. The plurality ofiterations may be performed to update the parameter values of the secondpreliminary model (or the intermediate model) until a terminationcondition is satisfied. If the termination condition is satisfied in acurrent iteration, the processing device 120B may designate the secondpreliminary model (or the intermediate model) obtained in the currentiteration as the second trained model. In some embodiments, the trainingof the second trained model may be performed in a similar manner as thetraining of the first trained model as described in connection with FIG.9 , the descriptions of which are not repeated here.

It should be noted that the above description regarding the process 1800is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

In some embodiments, the first trained model (or the second trainedmodel) may be updated from time to time, e.g., periodically or not,based on a sample set that is at least partially different from anoriginal sample set from which an original first trained model (or anoriginal second trained model) is determined. For instance, the firsttrained model (or the second trained model) may be updated based on asample set including new samples that are not in the original sampleset, samples processed using the model in connection with the originaltrained model of a prior version, or the like, or a combination thereof.In some embodiments, the determination and/or updating of the firsttrained model (or the second trained model) may be performed on aprocessing device, while the application of the first trained model (orthe second trained model) may be performed on a different processingdevice. In some embodiments, the determination and/or updating of thefirst trained model (or the second trained model) may be performed on aprocessing device of a system different than the medical system 100 or aserver different than a server including the processing device 120 onwhich the application of the first trained model (or the second trainedmodel) is performed. For instance, the determination and/or updating ofthe first trained model (or the second trained model) may be performedon a first system of a vendor who provides and/or maintains such amachine learning model and/or has access to training samples used todetermine and/or update the first trained model (or the second trainedmodel), while blood vessel parameters determination based on theprovided machine learning model may be performed on a second system of aclient of the vendor. In some embodiments, the determination and/orupdating of the first trained model (or the second trained model) may beperformed online in response to a request for determining blood vesselparameters. In some embodiments, the determination and/or updating ofthe first trained model (or the second trained model) may be performedoffline.

FIG. 19 is a schematic diagram illustrating an exemplary process forgenerating a second trained model according to some embodiments of thepresent disclosure.

As illustrated in FIG. 19 , in 1910, the processing device 1206 mayobtain a sample blood vessel image of a sample blood vessel as describedin connection with operation 1810. For example, the processing device1206 may determine a portion of a sample original image including thesample blood vessel as the sample blood vessel image. In 1920, theprocessing device 1206 may determine a sample blood vessel model of thesample blood vessel based on the sample blood vessel image as describedin connection with operation 1810. In 1930, the processing device 1206may obtain a sample mathematical expression corresponding to the sampleblood vessel model of the sample blood vessel as described in connectionwith operation 1910. For example, the processing device 1206 maydetermine the mathematical expression corresponding to the blood vesselmodel by performing a numerical processing operation on the blood vesselmodel.

In 1940, the processing device 120B may determine at least one referenceblood vessel parameter of the sample blood vessel based on the sampleblood vessel model and at least one sample blood flow parameter asdescribed in connection with operation 1910. In 1950, the processingdevice 120B may generate a second trained model by training a secondpreliminary model based on the sample mathematical expression and the atleast one reference blood vessel parameter of the sample blood vessel asdescribed in connection with operation 1920.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties such as molecular weight, reaction conditions, and so forth,used to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1. A method, implemented on a computing device having at least oneprocessor and at least one storage device, the method comprising:obtaining a blood vessel image of a target blood vessel; generating,based on the blood vessel image, a blood vessel model of the targetblood vessel, wherein the blood vessel model is a grid model; anddetermining, based at least on the blood vessel model, at least oneblood vessel parameter of the target blood vessel.
 2. The method ofclaim 1, wherein the generating, based on the blood vessel image, ablood vessel model of the target blood vessel comprises: determining atleast one model parameter of the blood vessel model based on featureinformation of the target blood vessel; and generating the blood vesselmodel by performing, based on the at least one model parameter, a griddivision on the blood vessel image.
 3. (canceled)
 4. The method of claim1, wherein the determining, based at least on the blood vessel model, atleast one blood vessel parameter of the target blood vessel comprises:obtaining at least one blood flow parameter of the target blood vessel;and determining, based on the blood vessel model and the at least oneblood flow parameter, the at least one blood vessel parameter of thetarget blood vessel using a first trained model.
 5. The method of claim4, wherein the determining, based on the blood vessel model and the atleast one blood flow parameter, the at least one blood vessel parameterof the target blood vessel using a first trained model comprises:obtaining at least one first grid node and at least one second grid nodeof the blood vessel model; determining, based on the at least one bloodflow parameter, an initial value of the at least one first grid node;determining, based on a preset value, an initial value of the at leastone second grid node; generating a preprocessed blood vessel model bypreprocessing, based on the initial value of the at least one first gridnode and the initial value of the at least one second grid node, theblood vessel model; and determining, based on the preprocessed bloodvessel model, the at least one blood vessel parameter of the targetblood vessel using the first trained model.
 6. The method of claim 4,wherein the first trained model is a graph neural network model.
 7. Themethod of claim 4, wherein the blood flow parameter includes at leastone of a blood density, a blood viscosity, an average blood flowvelocity, an average blood flow volume, a blood pressure, or a cardiacoutput.
 8. The method of claim 4, wherein the first trained model isobtained according to a training process including: obtaining aplurality of groups of first training samples, wherein each group of theplurality of groups of first training samples includes a preprocessedsample blood vessel model of a sample blood vessel and at least onereference blood vessel parameter of the sample blood vessel; andgenerating the first trained model by training a first preliminary modelusing the plurality of groups of first training samples.
 9. The methodof claim 8, wherein the obtaining a plurality of groups of firsttraining samples comprises: for each group of the plurality of groups offirst training samples, obtaining a sample blood vessel image of thesample blood vessel; determining, based on the sample blood vesselimage, a sample blood vessel model of the sample blood vessel; obtainingat least one sample blood flow parameter of the sample blood vessel; andgenerating the preprocessed sample blood vessel model by preprocessing,based on the at least one sample blood flow parameter, the sample bloodvessel model. 10-12. (canceled)
 13. The method of claim 1, wherein thedetermining, based at least on the blood vessel model, at least oneblood vessel parameter of the target blood vessel comprises: determininga mathematical expression corresponding to the blood vessel model; anddetermining, based on the mathematical expression, the at least oneblood vessel parameter of the target blood vessel using a second trainedmodel.
 14. The method of claim 13, wherein the determining amathematical expression corresponding to the blood vessel modelcomprises: determining the mathematical expression corresponding to theblood vessel model by performing a numerical processing operation on theblood vessel model.
 15. (canceled)
 16. The method of claim 13, whereinthe blood vessel model is a structured grid model, and the determining amathematical expression corresponding to the blood vessel modelcomprises: dividing the structured grid model into at least one firstlayer along an axial direction of the blood vessel model, the at leastone first layer including a plurality of first grids; and determiningthe mathematical expression based on first coordinate information of theplurality of first grids of the at least one first layer.
 17. The methodof claim 16, wherein the blood vessel model is a structured surface gridmodel, and the determining the mathematical expression based on firstcoordinate information of the plurality of first grids of the at leastone first layer comprises: for each of the at least one first layer,determining a layer vector corresponding to the first layer based on thefirst coordinate information of the plurality of first grids of thefirst layer; and determining a vector matrix corresponding to the bloodvessel model based on at least one layer vector corresponding to the atleast one first layer.
 18. The method of claim 16, wherein the bloodvessel model is a structured volume grid model, each first layerincludes a plurality of rows of grids, and the determining themathematical expression based on first coordinate information of theplurality of first grids of the at least one first layer comprises: foreach row of the plurality of rows in each first layer, determining a rowvector corresponding to the row based on the first coordinateinformation of the plurality of first grids of the row; for each firstlayer of the at least one first layer, determining a layer vector matrixcorresponding to the first layer based on a plurality of row vectorscorresponding to the plurality of rows in the first layer; anddetermining a vector matrix corresponding to the blood vessel modelbased on at least one layer vector matrix corresponding to the at leastone first layer.
 19. (canceled)
 20. The method of claim 13, wherein theblood vessel model is an unstructured grid model, and the determining amathematical expression corresponding to the blood vessel modelcomprises: mapping the unstructured grid model to a structured gridmodel; dividing the structured grid model into at least one second layeralong an axial direction of the structured grid model, the at least onesecond layer including a plurality of second grids; and determining themathematical expression based on second coordinate information of theplurality of second grids of the at least one second layer.
 21. Themethod of claim 13, wherein the blood vessel model is an unstructuredgrid model, the unstructured grid model includes a plurality of thirdgirds, and the determining a mathematical expression corresponding tothe blood vessel model comprises: determining the mathematicalexpression based on third coordinate information of the plurality ofthird girds.
 22. The method of claim 13, wherein the determining, basedon the mathematical expression, the at least one blood vessel parameterof the target blood vessel using a second trained model comprises:determining the at least one blood vessel parameter of the target bloodvessel by inputting the mathematical expression into the second trainedmodel.
 23. The method of claim 13, wherein the determining, based on themathematical expression, the at least one blood vessel parameter of thetarget blood vessel using a second trained model comprises: obtaining atleast one blood flow parameter of the target blood vessel; anddetermining the at least one blood vessel parameter of the target bloodvessel by inputting the mathematical expression and the at least oneblood flow parameter into the second trained model.
 24. The method ofclaim 13, wherein the second trained model is obtained according to atraining process including: obtaining a plurality of groups of secondtraining samples, wherein each group of the plurality of groups ofsecond training samples includes a sample mathematical expressioncorresponding to a sample blood vessel model of a sample blood vesseland at least one reference blood vessel parameter of the sample bloodvessel; and generating the second trained model by training a secondpreliminary model using the plurality of groups of second trainingsamples. 25-27. (canceled)
 28. A system, comprising: at least onestorage device storing executable instructions, and at least oneprocessor in communication with the at least one storage device, whereinwhen executing the executable instructions, the at least one processorcauses the system to perform operations including: obtaining a bloodvessel image of a target blood vessel; generating, based on the bloodvessel image, a blood vessel model of the target blood vessel, whereinthe blood vessel model is a grid model; and determining, based at leaston the blood vessel model, at least one blood vessel parameter of thetarget blood vessel.
 29. A non-transitory computer readable medium,comprising at least one set of instructions, wherein when executed byone or more processors of a computing device, the at least one set ofinstructions causes the computing device to perform a method, the methodcomprising: obtaining a blood vessel image of a target blood vessel;generating, based on the blood vessel image, a blood vessel model of thetarget blood vessel, wherein the blood vessel model is a grid model; anddetermining, based at least on the blood vessel model, at least oneblood vessel parameter of the target blood vessel.